diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index eb654e1..f39b4ca 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -6,6 +6,7 @@ Here are some tasks to complete before merging this PR:
## Jupyter
+- [ ] Add references to the bottom of the notebook.
- [ ] Make sure it runs on cpu and gpu
- [ ] Comment the shapes of any numpy arrays or torch tensors. Make assertions on the output.
- [ ] Functions that have more than one parameter should have a `*` before the first or second parameter to force the user to use named arguments, unless there is only one parameter.
@@ -30,6 +31,6 @@ if __name__ == '__main__':
## Logseq
- [ ] Make logseq notes and flash cards.
-- [ ] Do not use logseq aliases so that the graph looks clean and its more navigable.
- [ ] Use singular nouns for tags.
-- [ ] Use spaces in filenames instead of `-` or `_` just so that you don't have to use aliases (ugly I know).
+- [ ] Use `-` or `_` in filenames instead of spaces.
+- [ ] Use aliases for spaces and plurals.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index ab974fd..74cc46e 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -42,5 +42,7 @@ repos:
- id: enforce-ascii
files: notes/pages/.*\.md
- id: mdlinker
+ files: notes/pages/.*\.md
args:
- "--fix"
+ - "--allow-dirty"
diff --git a/continuing_education/lib/episodes.py b/continuing_education/lib/episodes.py
index 6711909..2e70bb7 100644
--- a/continuing_education/lib/episodes.py
+++ b/continuing_education/lib/episodes.py
@@ -21,6 +21,9 @@ class SARSA:
next_action: Action | None = None
action_log_prob: LogProb = LogProb(tensor(0.0))
+ def to_sars(self) -> "SARS":
+ return SARS.from_sarsa(self)
+
def collect_episode(
*, env: Env, policy: DiscreteActionPolicyInterface, max_t: int, **policy_kwargs
@@ -51,3 +54,22 @@ def collect_episode(
assert next_action is not None
state, action, action_logprob = next_state, next_action, next_action_logprob
+
+
+@dataclass
+class SARS:
+ state: State
+ action: Action
+ reward: float
+ next_state: State
+ done: bool
+
+ @staticmethod
+ def from_sarsa(sarsa: SARSA) -> "SARS":
+ return SARS(
+ state=sarsa.state,
+ action=sarsa.action,
+ reward=sarsa.reward,
+ next_state=sarsa.next_state,
+ done=sarsa.done,
+ )
diff --git a/continuing_education/lib/experiments/manager.py b/continuing_education/lib/experiments/manager.py
index 7af785e..e069f86 100644
--- a/continuing_education/lib/experiments/manager.py
+++ b/continuing_education/lib/experiments/manager.py
@@ -20,6 +20,16 @@ def __init__(
self.file = file
self.primary_metric = primary_metric
+ # Check for unstaged changes
+ # This is being moved to __init__ because we usually generate some notebook changes when running experiments, like plots and the like
+ self.repo = Repo(
+ path=self.file.absolute().parent, search_parent_directories=True
+ )
+ if self.repo.is_dirty(untracked_files=True):
+ warnings.warn(
+ "There are unstaged changes in the repository. Please commit or stage them before running the experiment manager."
+ )
+
@property
def is_jupytext(self) -> bool:
return (
@@ -34,22 +44,17 @@ def run_jupytext_sync(self):
subprocess.run(cmd, check=True)
def commit(self, metrics: dict[str, Any] | None = None):
- repo = Repo(path=self.file.absolute().parent, search_parent_directories=True)
if metrics is None:
metrics = {}
self.run_jupytext_sync()
# Staging files
- files = [self.file.relative_to(repo.working_dir)]
+ files = [self.file.relative_to(self.repo.working_dir)]
if self.is_jupytext:
- files.append(self.file.relative_to(repo.working_dir).with_suffix(".py"))
- repo.index.add(files)
-
- # Check for unstaged changes
- if repo.is_dirty(untracked_files=True):
- warnings.warn(
- "There are unstaged changes in the repository. Please commit or stage them before running the experiment manager."
+ files.append(
+ self.file.relative_to(self.repo.working_dir).with_suffix(".py")
)
+ self.repo.index.add(files)
# Committing changes
if self.primary_metric in metrics:
@@ -57,6 +62,6 @@ def commit(self, metrics: dict[str, Any] | None = None):
else:
commit_message = f"Experiment: {self.name}"
detailed_message = f"{self.description}\n\nResults:\n{pformat(metrics)}".strip()
- repo.index.commit(
+ self.repo.index.commit(
message=commit_message + "\n\n" + detailed_message, skip_hooks=True
)
diff --git a/notes/pages/derivatives.md b/continuing_education/policy_gradient_methods/actor_critic/__init__.py
similarity index 100%
rename from notes/pages/derivatives.md
rename to continuing_education/policy_gradient_methods/actor_critic/__init__.py
diff --git a/continuing_education/policy_gradient_methods/actor_critic/a2c.ipynb b/continuing_education/policy_gradient_methods/actor_critic/a2c.ipynb
new file mode 100644
index 0000000..a377d76
--- /dev/null
+++ b/continuing_education/policy_gradient_methods/actor_critic/a2c.ipynb
@@ -0,0 +1,161 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " __this_file = (\n",
+ " Path().resolve() / \"actor_critic.ipynb\"\n",
+ " ) # jupyter does not have __file__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "cuda:0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "\n",
+ "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " print(DEVICE)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "lines_to_next_cell": 2
+ },
+ "outputs": [],
+ "source": [
+ "from torch import nn\n",
+ "\n",
+ "from continuing_education.policy_gradient_methods.reinforce import SamplePolicy"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "lines_to_next_cell": 2
+ },
+ "source": [
+ "# Actor Critic\n",
+ "\n",
+ "So in the REINFORCE algorithm, we experimented with batch updates and saw that while it slowed down the code it led to serious improvements. The size of batching needed to introduce stability in learning is called the sample efficiency of an algorithm, and REINFORCE is particularly bad at it. In Actor-Critic we introduce asynchronicity into the environments episode generation and the policy update.\n",
+ "\n",
+ "The Actor-Critic method uses asynchronicity by having neural network \"servers\" which spawn copies of themselves to each interact with copies of the environment. Trajectories are generated by these servers and the gradient updates are sent back to the main server, which averages them and updates the policy. This is a form of parallelism, and it is a very powerful tool in reinforcement learning.\n",
+ "\n",
+ "Another concept that Actor Critic introduces is that it hybridizes policy based methods (the actor) and value based methods (the critic). This gives you many of the advantages of both methods.\n",
+ "\n",
+ "The huggingface tutorial on A2C wants us to use stable-baselines3, but I call that cheating. We will implement A2C from scratch using PyTorch."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Actor(SamplePolicy):\n",
+ " \"\"\"This is exactly the same as our SamplePolicy from REINFORCE, but we need to add a few methods.\"\"\"\n",
+ "\n",
+ " def copy(self) -> \"Actor\":\n",
+ " new_actor = Actor(\n",
+ " state_size=self.state_size,\n",
+ " action_size=self.action_size,\n",
+ " hidden_sizes=self.hidden_sizes,\n",
+ " )\n",
+ " new_actor.network.load_state_dict(self.network.state_dict())\n",
+ " return new_actor\n",
+ "\n",
+ " def update(self, gradients: list[nn.Module]):\n",
+ " raise NotImplementedError(\"Unsure how this works\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "lines_to_next_cell": 2
+ },
+ "outputs": [],
+ "source": [
+ "from continuing_education.value_based_methods.dqn.dqn import QLearningModel\n",
+ "\n",
+ "\n",
+ "class Critic(QLearningModel):\n",
+ " def copy(self) -> \"Critic\":\n",
+ " new_critic = Critic(\n",
+ " state_size=self.state_size,\n",
+ " action_size=self.action_size,\n",
+ " hidden_sizes=self.hidden_sizes,\n",
+ " )\n",
+ " new_critic.network.load_state_dict(self.network.state_dict())\n",
+ " return new_critic\n",
+ "\n",
+ " def update(self, gradients: list[nn.Module]):\n",
+ " raise NotImplementedError(\"Unsure how this works\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# References\n",
+ "\n",
+ "1. Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., … Kavukcuoglu, K. (2016). Asynchronous Methods for Deep Reinforcement Learning. arXiv [Cs.LG]. Retrieved from http://arxiv.org/abs/1602.01783\n",
+ "2. UNIT 6. ACTOR CRITIC METHODS WITH ROBOTICS ENVIRONMENTS. Hugging Face. (n.d.). https://huggingface.co/learn/deep-rl-course/unit6/introduction"
+ ]
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "formats": "ipynb,py:percent"
+ },
+ "kernelspec": {
+ "display_name": "continuing_education",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/continuing_education/policy_gradient_methods/actor_critic/a2c.py b/continuing_education/policy_gradient_methods/actor_critic/a2c.py
new file mode 100644
index 0000000..4250bfd
--- /dev/null
+++ b/continuing_education/policy_gradient_methods/actor_critic/a2c.py
@@ -0,0 +1,93 @@
+# ---
+# jupyter:
+# jupytext:
+# formats: ipynb,py:percent
+# text_representation:
+# extension: .py
+# format_name: percent
+# format_version: '1.3'
+# jupytext_version: 1.16.7
+# kernelspec:
+# display_name: continuing_education
+# language: python
+# name: python3
+# ---
+
+# %%
+# %load_ext autoreload
+# %autoreload 2
+
+# %%
+from pathlib import Path
+
+if __name__ == "__main__":
+ __this_file = (
+ Path().resolve() / "actor_critic.ipynb"
+ ) # jupyter does not have __file__
+
+# %%
+import torch
+
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+if __name__ == "__main__":
+ print(DEVICE)
+
+# %%
+from torch import nn
+
+from continuing_education.policy_gradient_methods.reinforce import SamplePolicy
+
+
+# %% [markdown]
+# # Actor Critic
+#
+# So in the REINFORCE algorithm, we experimented with batch updates and saw that while it slowed down the code it led to serious improvements. The size of batching needed to introduce stability in learning is called the sample efficiency of an algorithm, and REINFORCE is particularly bad at it. In Actor-Critic we introduce asynchronicity into the environments episode generation and the policy update.
+#
+# The Actor-Critic method uses asynchronicity by having neural network "servers" which spawn copies of themselves to each interact with copies of the environment. Trajectories are generated by these servers and the gradient updates are sent back to the main server, which averages them and updates the policy. This is a form of parallelism, and it is a very powerful tool in reinforcement learning.
+#
+# Another concept that Actor Critic introduces is that it hybridizes policy based methods (the actor) and value based methods (the critic). This gives you many of the advantages of both methods.
+#
+# The huggingface tutorial on A2C wants us to use stable-baselines3, but I call that cheating. We will implement A2C from scratch using PyTorch.
+
+
+# %%
+class Actor(SamplePolicy):
+ """This is exactly the same as our SamplePolicy from REINFORCE, but we need to add a few methods."""
+
+ def copy(self) -> "Actor":
+ new_actor = Actor(
+ state_size=self.state_size,
+ action_size=self.action_size,
+ hidden_sizes=self.hidden_sizes,
+ )
+ new_actor.network.load_state_dict(self.network.state_dict())
+ return new_actor
+
+ def update(self, gradients: list[nn.Module]):
+ raise NotImplementedError("Unsure how this works")
+
+
+# %%
+from continuing_education.value_based_methods.dqn.dqn import QLearningModel
+
+
+class Critic(QLearningModel):
+ def copy(self) -> "Critic":
+ new_critic = Critic(
+ state_size=self.state_size,
+ action_size=self.action_size,
+ hidden_sizes=self.hidden_sizes,
+ )
+ new_critic.network.load_state_dict(self.network.state_dict())
+ return new_critic
+
+ def update(self, gradients: list[nn.Module]):
+ raise NotImplementedError("Unsure how this works")
+
+
+# %% [markdown]
+# # References
+#
+# 1. Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., … Kavukcuoglu, K. (2016). Asynchronous Methods for Deep Reinforcement Learning. arXiv [Cs.LG]. Retrieved from http://arxiv.org/abs/1602.01783
+# 2. UNIT 6. ACTOR CRITIC METHODS WITH ROBOTICS ENVIRONMENTS. Hugging Face. (n.d.). https://huggingface.co/learn/deep-rl-course/unit6/introduction
diff --git a/continuing_education/policy_gradient_methods/reinforce/reinforce.ipynb b/continuing_education/policy_gradient_methods/reinforce/reinforce.ipynb
index 987bdc2..19460ca 100644
--- a/continuing_education/policy_gradient_methods/reinforce/reinforce.ipynb
+++ b/continuing_education/policy_gradient_methods/reinforce/reinforce.ipynb
@@ -3,7 +3,6 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "6c2cd6ae",
"metadata": {},
"outputs": [],
"source": [
@@ -407,7 +406,6 @@
{
"cell_type": "code",
"execution_count": 11,
- "id": "ce0812a3",
"metadata": {
"lines_to_next_cell": 2
},
@@ -79637,7 +79635,7 @@
"formats": "ipynb,py:percent"
},
"kernelspec": {
- "display_name": "continuing-education-vJKa4-To-py3.10",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -79651,9 +79649,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.6"
+ "version": "3.11.9"
}
},
"nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
}
diff --git a/continuing_education/policy_gradient_methods/reinforce/reinforce.py b/continuing_education/policy_gradient_methods/reinforce/reinforce.py
index 21a8932..8123a9f 100644
--- a/continuing_education/policy_gradient_methods/reinforce/reinforce.py
+++ b/continuing_education/policy_gradient_methods/reinforce/reinforce.py
@@ -8,7 +8,7 @@
# format_version: '1.3'
# jupytext_version: 1.16.7
# kernelspec:
-# display_name: continuing-education-vJKa4-To-py3.10
+# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
diff --git a/continuing_education/value_based_methods/dqn/dqn.ipynb b/continuing_education/value_based_methods/dqn/dqn.ipynb
index 05c4b33..41becca 100644
--- a/continuing_education/value_based_methods/dqn/dqn.ipynb
+++ b/continuing_education/value_based_methods/dqn/dqn.ipynb
@@ -347,7 +347,6 @@
" batch_size: int,\n",
" exploration_rate_decay: float,\n",
") -> list[Reward]:\n",
- " \"\"\"Algorithm 1 REINFORCE\"\"\"\n",
" assert gamma <= 1, \"Gamma should be less than or equal to 1\"\n",
" assert gamma > 0, \"Gamma should be greater than 0\"\n",
" assert num_episodes > 0, \"Number of episodes should be greater than 0\"\n",
@@ -448,7 +447,7 @@
"from continuing_education.policy_gradient_methods.reinforce.reinforce import MockEnv\n",
"\n",
"\n",
- "def test_reinforce_train() -> None:\n",
+ "def test_dqn_train() -> None:\n",
" \"\"\"Test the reinforce training loop on the mock environment.\"\"\"\n",
" env = MockEnv(max_steps=10)\n",
" value_network = QLearningModel(state_size=1, action_size=2, hidden_sizes=[16, 16])\n",
@@ -472,8 +471,8 @@
"\n",
"if __name__ == \"__main__\":\n",
" for _ in range(3):\n",
- " test_reinforce_train()\n",
- " print(\"test_reinforce_train passed\")"
+ " test_dqn_train()\n",
+ " print(\"test_dqn_train passed\")"
]
},
{
@@ -9342,7 +9341,7 @@
"formats": "ipynb,py:percent"
},
"kernelspec": {
- "display_name": "continuing_education",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -9356,9 +9355,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.6"
+ "version": "3.11.9"
}
},
"nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
}
diff --git a/continuing_education/value_based_methods/dqn/dqn.py b/continuing_education/value_based_methods/dqn/dqn.py
index 120a7f2..a58324b 100644
--- a/continuing_education/value_based_methods/dqn/dqn.py
+++ b/continuing_education/value_based_methods/dqn/dqn.py
@@ -8,7 +8,7 @@
# format_version: '1.3'
# jupytext_version: 1.16.7
# kernelspec:
-# display_name: continuing_education
+# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
@@ -269,7 +269,6 @@ def dqn_train(
batch_size: int,
exploration_rate_decay: float,
) -> list[Reward]:
- """Algorithm 1 REINFORCE"""
assert gamma <= 1, "Gamma should be less than or equal to 1"
assert gamma > 0, "Gamma should be greater than 0"
assert num_episodes > 0, "Number of episodes should be greater than 0"
@@ -301,7 +300,7 @@ def dqn_train(
from continuing_education.policy_gradient_methods.reinforce.reinforce import MockEnv
-def test_reinforce_train() -> None:
+def test_dqn_train() -> None:
"""Test the reinforce training loop on the mock environment."""
env = MockEnv(max_steps=10)
value_network = QLearningModel(state_size=1, action_size=2, hidden_sizes=[16, 16])
@@ -325,8 +324,8 @@ def test_reinforce_train() -> None:
if __name__ == "__main__":
for _ in range(3):
- test_reinforce_train()
- print("test_reinforce_train passed")
+ test_dqn_train()
+ print("test_dqn_train passed")
# %%
from continuing_education.policy_gradient_methods.reinforce.reinforce import (
diff --git a/notes/pages/distribution.md b/continuing_education/value_based_methods/duelingdqn/__init__.py
similarity index 100%
rename from notes/pages/distribution.md
rename to continuing_education/value_based_methods/duelingdqn/__init__.py
diff --git a/continuing_education/value_based_methods/duelingdqn/duelingdqn.ipynb b/continuing_education/value_based_methods/duelingdqn/duelingdqn.ipynb
new file mode 100644
index 0000000..e4f3e84
--- /dev/null
+++ b/continuing_education/value_based_methods/duelingdqn/duelingdqn.ipynb
@@ -0,0 +1,18089 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " __this_file = (\n",
+ " Path().resolve() / \"duelingdqn.ipynb\"\n",
+ " ) # jupyter does not have __file__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "cpu\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "\n",
+ "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " print(DEVICE)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "lines_to_next_cell": 2
+ },
+ "outputs": [],
+ "source": [
+ "from torch import nn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from continuing_education.value_based_methods.dqn.dqn import QLearningModel\n",
+ "from enum import Enum\n",
+ "\n",
+ "\n",
+ "class AdvantageType(str, Enum):\n",
+ " AVG = \"avg\"\n",
+ " MAX = \"max\"\n",
+ "\n",
+ "\n",
+ "class DuelingQLearningModel(QLearningModel):\n",
+ " def __init__(\n",
+ " self,\n",
+ " *,\n",
+ " state_size: int,\n",
+ " action_size: int,\n",
+ " hidden_sizes: list[int],\n",
+ " advantage_hidden_sizes: list[int],\n",
+ " value_hidden_sizes: list[int],\n",
+ " adv_type: AdvantageType,\n",
+ " ) -> None:\n",
+ " \"\"\"\n",
+ " Notice that this is exactly the same as the Policy network from REINFORCE, because\n",
+ " we are still starting from the state and outputting an action. The difference is that\n",
+ " we will not softmax the output, because its not a probability distribution, but rather\n",
+ " a regressor that outputs the Q value of each action.\n",
+ " \"\"\"\n",
+ " super().__init__(\n",
+ " state_size=state_size, action_size=action_size, hidden_sizes=hidden_sizes\n",
+ " )\n",
+ " assert len(hidden_sizes) > 0, \"Need at least one hidden layer\"\n",
+ " assert len(advantage_hidden_sizes) > 0, (\n",
+ " \"Need at least one advantage hidden layer\"\n",
+ " )\n",
+ " assert len(value_hidden_sizes) > 0, \"Need at least one value hidden layer\"\n",
+ " self.state_size = state_size\n",
+ " self.action_size = action_size\n",
+ " self.hidden_sizes = hidden_sizes\n",
+ " self.advantage_hidden_sizes = advantage_hidden_sizes\n",
+ " self.value_hidden_sizes = value_hidden_sizes\n",
+ " self.adv_type = adv_type\n",
+ "\n",
+ " # Dimensions in the network are (batch_size, input_size, output_size)\n",
+ " network: list[nn.Module] = []\n",
+ " network.append(\n",
+ " nn.Linear(state_size, hidden_sizes[0])\n",
+ " ) # Shape: (:, state_size, hidden_sizes[0])\n",
+ " network.append(nn.ReLU())\n",
+ " for i in range(len(hidden_sizes) - 1):\n",
+ " network.append(\n",
+ " nn.Linear(hidden_sizes[i], hidden_sizes[i + 1])\n",
+ " ) # Shape: (:, hidden_sizes[i], hidden_sizes[i+1])\n",
+ " network.append(nn.ReLU())\n",
+ " self.network = nn.Sequential(*network).to(DEVICE)\n",
+ "\n",
+ " # Now we are going to split into the value and advantage branches\n",
+ " # Advantage Network\n",
+ " advantage_network: list[nn.Module] = []\n",
+ " advantage_network.append(\n",
+ " nn.Linear(hidden_sizes[-1], advantage_hidden_sizes[0])\n",
+ " ) # Shape: (:, hidden_sizes[-1], advantage_hidden_sizes[0])\n",
+ " advantage_network.append(nn.ReLU())\n",
+ " for i in range(len(advantage_hidden_sizes) - 1):\n",
+ " advantage_network.append(\n",
+ " nn.Linear(advantage_hidden_sizes[i], advantage_hidden_sizes[i + 1])\n",
+ " ) # Shape: (:, advantage_hidden_sizes[i], advantage_hidden_sizes[i + 1])\n",
+ " advantage_network.append(nn.ReLU())\n",
+ " advantage_network.append(\n",
+ " nn.Linear(advantage_hidden_sizes[-1], action_size)\n",
+ " ) # Shape: (:, advantage_hidden_sizes[-1], action_size)\n",
+ " self.advantage_network = nn.Sequential(*advantage_network).to(DEVICE)\n",
+ "\n",
+ " # Value Network\n",
+ " value_network: list[nn.Module] = []\n",
+ " value_network.append(\n",
+ " nn.Linear(hidden_sizes[-1], value_hidden_sizes[0])\n",
+ " ) # Shape: (:, hidden_sizes[-1], value_hidden_sizes[0])\n",
+ " value_network.append(nn.ReLU())\n",
+ " for i in range(len(advantage_hidden_sizes) - 1):\n",
+ " value_network.append(\n",
+ " nn.Linear(value_hidden_sizes[i], value_hidden_sizes[i + 1])\n",
+ " ) # Shape: (:, value_hidden_sizes[i], value_hidden_sizes[i + 1])\n",
+ " value_network.append(nn.ReLU())\n",
+ " value_network.append(\n",
+ " nn.Linear(value_hidden_sizes[-1], 1)\n",
+ " ) # Shape: (:, value_hidden_sizes[-1], 1)\n",
+ " self.value_network = nn.Sequential(*value_network).to(DEVICE)\n",
+ "\n",
+ " def forward(self, state: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Takes a state tensor and returns logits along the action space\"\"\"\n",
+ " state = state.to(DEVICE)\n",
+ " network_out = self.network(state)\n",
+ " advantage_out = self.advantage_network(network_out)\n",
+ " value_out = self.value_network(network_out)\n",
+ "\n",
+ " # These \"recreate\" the Q function via a value function and an advantage function\n",
+ " if self.adv_type == AdvantageType.AVG:\n",
+ " advAverage = torch.mean(advantage_out, dim=1, keepdim=True)\n",
+ " q = value_out + advantage_out - advAverage\n",
+ " elif self.adv_type == AdvantageType.MAX:\n",
+ " advMax, _ = torch.max(advantage_out, dim=1, keepdim=True)\n",
+ " q = value_out + advantage_out - advMax\n",
+ " else:\n",
+ " raise KeyError(f\"{self.adv_type} not recognized\")\n",
+ " return q # Shape: (:, action_size)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Testing the new DDQN network the same as we did in the DQN notebook. For some reason this doesn't always get straight 10's as last time. We will forgive it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c969e96d0c3941fbb3a511117b8b46b2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/100 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "test_ddqn_train passed\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8a67ada35350469186c86a05ba2ec805",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/100 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "test_ddqn_train passed\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "db57a49408874a4381848fd8cf866e4a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/100 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "test_ddqn_train passed\n"
+ ]
+ }
+ ],
+ "source": [
+ "from continuing_education.policy_gradient_methods.reinforce.reinforce import MockEnv\n",
+ "from continuing_education.value_based_methods.dqn.dqn import (\n",
+ " dqn_train,\n",
+ " ActionReplayMemory,\n",
+ ")\n",
+ "from torch import optim\n",
+ "\n",
+ "\n",
+ "def test_ddqn_train() -> None:\n",
+ " \"\"\"Test the reinforce training loop on the mock environment.\"\"\"\n",
+ " env = MockEnv(max_steps=10)\n",
+ " value_network = DuelingQLearningModel(\n",
+ " state_size=1,\n",
+ " action_size=2,\n",
+ " hidden_sizes=[16, 16],\n",
+ " advantage_hidden_sizes=[16],\n",
+ " value_hidden_sizes=[16],\n",
+ " adv_type=AdvantageType.MAX,\n",
+ " )\n",
+ " optimizer = optim.Adam(value_network.parameters(), lr=1e-3)\n",
+ " memory = ActionReplayMemory(max_size=1000)\n",
+ " scores = dqn_train(\n",
+ " env=env,\n",
+ " value_network=value_network,\n",
+ " optimizer=optimizer,\n",
+ " memory=memory,\n",
+ " gamma=0.999,\n",
+ " num_episodes=100,\n",
+ " max_t=10,\n",
+ " batch_size=50,\n",
+ " exploration_rate_decay=0.96,\n",
+ " )\n",
+ " assert sum([score == 10 for score in scores[90:]]) >= 8, (\n",
+ " f\"The last 10 scores should be 10, got: {scores[90:]}\"\n",
+ " )\n",
+ "\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " for _ in range(3):\n",
+ " test_ddqn_train()\n",
+ " print(\"test_ddqn_train passed\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "State size: (4,)\n",
+ "Action size: 2\n",
+ "Example state: (array([ 0.0499102 , -0.02624804, 0.004836 , -0.02674999], dtype=float32), {})\n",
+ "Action return: (array([ 0.04938524, 0.16880423, 0.004301 , -0.3179032 ], dtype=float32), 1.0, False, False, {})\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/ryan.peach/Documents/ryanpeach/continuing_education/.venv/lib/python3.11/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`. (Deprecated NumPy 1.24)\n",
+ " if not isinstance(terminated, (bool, np.bool8)):\n"
+ ]
+ }
+ ],
+ "source": [
+ "from continuing_education.policy_gradient_methods.reinforce.reinforce import (\n",
+ " get_environment_space,\n",
+ ")\n",
+ "\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " OBSERVATION_SPACE_SHAPE, ACTION_SPACE_SIZE = get_environment_space(\"CartPole-v1\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Lastly, because Q Learning can not learn stochastic policies, we need to incentivize the network to explore during training or else we will not learn a good value function. We do this by adding noise to the action selection during training, and reducing it over time. This is called epsilon greedy exploration. We will start with an epsilon of 1, meaning we will always take a random action, and decay it to 0 exponentially over the course of training. You want to pick a discount factor which converges to 0 just before the end of training usually."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/ryan.peach/Documents/ryanpeach/continuing_education/continuing_education/lib/experiments/manager.py:29: UserWarning: There are unstaged changes in the repository. Please commit or stage them before running the experiment manager.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "90eac4f99a2746d1bd7fd1700b33dea6",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/500 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/ryan.peach/Documents/ryanpeach/continuing_education/.venv/lib/python3.11/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`. (Deprecated NumPy 1.24)\n",
+ " if not isinstance(terminated, (bool, np.bool8)):\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "mode": "lines",
+ "name": "Exploration Rate",
+ "type": "scatter",
+ "x": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 60,
+ 61,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 81,
+ 82,
+ 83,
+ 84,
+ 85,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 91,
+ 92,
+ 93,
+ 94,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 108,
+ 109,
+ 110,
+ 111,
+ 112,
+ 113,
+ 114,
+ 115,
+ 116,
+ 117,
+ 118,
+ 119,
+ 120,
+ 121,
+ 122,
+ 123,
+ 124,
+ 125,
+ 126,
+ 127,
+ 128,
+ 129,
+ 130,
+ 131,
+ 132,
+ 133,
+ 134,
+ 135,
+ 136,
+ 137,
+ 138,
+ 139,
+ 140,
+ 141,
+ 142,
+ 143,
+ 144,
+ 145,
+ 146,
+ 147,
+ 148,
+ 149,
+ 150,
+ 151,
+ 152,
+ 153,
+ 154,
+ 155,
+ 156,
+ 157,
+ 158,
+ 159,
+ 160,
+ 161,
+ 162,
+ 163,
+ 164,
+ 165,
+ 166,
+ 167,
+ 168,
+ 169,
+ 170,
+ 171,
+ 172,
+ 173,
+ 174,
+ 175,
+ 176,
+ 177,
+ 178,
+ 179,
+ 180,
+ 181,
+ 182,
+ 183,
+ 184,
+ 185,
+ 186,
+ 187,
+ 188,
+ 189,
+ 190,
+ 191,
+ 192,
+ 193,
+ 194,
+ 195,
+ 196,
+ 197,
+ 198,
+ 199,
+ 200,
+ 201,
+ 202,
+ 203,
+ 204,
+ 205,
+ 206,
+ 207,
+ 208,
+ 209,
+ 210,
+ 211,
+ 212,
+ 213,
+ 214,
+ 215,
+ 216,
+ 217,
+ 218,
+ 219,
+ 220,
+ 221,
+ 222,
+ 223,
+ 224,
+ 225,
+ 226,
+ 227,
+ 228,
+ 229,
+ 230,
+ 231,
+ 232,
+ 233,
+ 234,
+ 235,
+ 236,
+ 237,
+ 238,
+ 239,
+ 240,
+ 241,
+ 242,
+ 243,
+ 244,
+ 245,
+ 246,
+ 247,
+ 248,
+ 249,
+ 250,
+ 251,
+ 252,
+ 253,
+ 254,
+ 255,
+ 256,
+ 257,
+ 258,
+ 259,
+ 260,
+ 261,
+ 262,
+ 263,
+ 264,
+ 265,
+ 266,
+ 267,
+ 268,
+ 269,
+ 270,
+ 271,
+ 272,
+ 273,
+ 274,
+ 275,
+ 276,
+ 277,
+ 278,
+ 279,
+ 280,
+ 281,
+ 282,
+ 283,
+ 284,
+ 285,
+ 286,
+ 287,
+ 288,
+ 289,
+ 290,
+ 291,
+ 292,
+ 293,
+ 294,
+ 295,
+ 296,
+ 297,
+ 298,
+ 299,
+ 300,
+ 301,
+ 302,
+ 303,
+ 304,
+ 305,
+ 306,
+ 307,
+ 308,
+ 309,
+ 310,
+ 311,
+ 312,
+ 313,
+ 314,
+ 315,
+ 316,
+ 317,
+ 318,
+ 319,
+ 320,
+ 321,
+ 322,
+ 323,
+ 324,
+ 325,
+ 326,
+ 327,
+ 328,
+ 329,
+ 330,
+ 331,
+ 332,
+ 333,
+ 334,
+ 335,
+ 336,
+ 337,
+ 338,
+ 339,
+ 340,
+ 341,
+ 342,
+ 343,
+ 344,
+ 345,
+ 346,
+ 347,
+ 348,
+ 349,
+ 350,
+ 351,
+ 352,
+ 353,
+ 354,
+ 355,
+ 356,
+ 357,
+ 358,
+ 359,
+ 360,
+ 361,
+ 362,
+ 363,
+ 364,
+ 365,
+ 366,
+ 367,
+ 368,
+ 369,
+ 370,
+ 371,
+ 372,
+ 373,
+ 374,
+ 375,
+ 376,
+ 377,
+ 378,
+ 379,
+ 380,
+ 381,
+ 382,
+ 383,
+ 384,
+ 385,
+ 386,
+ 387,
+ 388,
+ 389,
+ 390,
+ 391,
+ 392,
+ 393,
+ 394,
+ 395,
+ 396,
+ 397,
+ 398,
+ 399,
+ 400,
+ 401,
+ 402,
+ 403,
+ 404,
+ 405,
+ 406,
+ 407,
+ 408,
+ 409,
+ 410,
+ 411,
+ 412,
+ 413,
+ 414,
+ 415,
+ 416,
+ 417,
+ 418,
+ 419,
+ 420,
+ 421,
+ 422,
+ 423,
+ 424,
+ 425,
+ 426,
+ 427,
+ 428,
+ 429,
+ 430,
+ 431,
+ 432,
+ 433,
+ 434,
+ 435,
+ 436,
+ 437,
+ 438,
+ 439,
+ 440,
+ 441,
+ 442,
+ 443,
+ 444,
+ 445,
+ 446,
+ 447,
+ 448,
+ 449,
+ 450,
+ 451,
+ 452,
+ 453,
+ 454,
+ 455,
+ 456,
+ 457,
+ 458,
+ 459,
+ 460,
+ 461,
+ 462,
+ 463,
+ 464,
+ 465,
+ 466,
+ 467,
+ 468,
+ 469,
+ 470,
+ 471,
+ 472,
+ 473,
+ 474,
+ 475,
+ 476,
+ 477,
+ 478,
+ 479,
+ 480,
+ 481,
+ 482,
+ 483,
+ 484,
+ 485,
+ 486,
+ 487,
+ 488,
+ 489,
+ 490,
+ 491,
+ 492,
+ 493,
+ 494,
+ 495,
+ 496,
+ 497,
+ 498,
+ 499
+ ],
+ "xaxis": "x",
+ "y": [
+ 1,
+ 0.99,
+ 0.9801,
+ 0.970299,
+ 0.96059601,
+ 0.9509900499,
+ 0.941480149401,
+ 0.93206534790699,
+ 0.92274469442792,
+ 0.9135172474836408,
+ 0.9043820750088044,
+ 0.8953382542587163,
+ 0.8863848717161291,
+ 0.8775210229989678,
+ 0.8687458127689781,
+ 0.8600583546412883,
+ 0.8514577710948754,
+ 0.8429431933839266,
+ 0.8345137614500874,
+ 0.8261686238355865,
+ 0.8179069375972307,
+ 0.8097278682212583,
+ 0.8016305895390458,
+ 0.7936142836436553,
+ 0.7856781408072188,
+ 0.7778213593991465,
+ 0.7700431458051551,
+ 0.7623427143471035,
+ 0.7547192872036325,
+ 0.7471720943315961,
+ 0.7397003733882802,
+ 0.7323033696543974,
+ 0.7249803359578534,
+ 0.7177305325982748,
+ 0.7105532272722921,
+ 0.7034476949995692,
+ 0.6964132180495735,
+ 0.6894490858690777,
+ 0.682554595010387,
+ 0.6757290490602831,
+ 0.6689717585696803,
+ 0.6622820409839835,
+ 0.6556592205741436,
+ 0.6491026283684022,
+ 0.6426116020847181,
+ 0.6361854860638709,
+ 0.6298236312032323,
+ 0.6235253948912,
+ 0.617290140942288,
+ 0.6111172395328651,
+ 0.6050060671375365,
+ 0.5989560064661611,
+ 0.5929664464014994,
+ 0.5870367819374844,
+ 0.5811664141181095,
+ 0.5753547499769285,
+ 0.5696012024771592,
+ 0.5639051904523876,
+ 0.5582661385478638,
+ 0.5526834771623851,
+ 0.5471566423907612,
+ 0.5416850759668536,
+ 0.536268225207185,
+ 0.5309055429551132,
+ 0.525596487525562,
+ 0.5203405226503064,
+ 0.5151371174238033,
+ 0.5099857462495653,
+ 0.5048858887870696,
+ 0.4998370298991989,
+ 0.49483865960020695,
+ 0.4898902730042049,
+ 0.4849913702741629,
+ 0.4801414565714212,
+ 0.475340042005707,
+ 0.47058664158564995,
+ 0.4658807751697934,
+ 0.4612219674180955,
+ 0.45660974774391455,
+ 0.4520436502664754,
+ 0.44752321376381066,
+ 0.44304798162617254,
+ 0.4386175018099108,
+ 0.4342313267918117,
+ 0.4298890135238936,
+ 0.42559012338865465,
+ 0.4213342221547681,
+ 0.4171208799332205,
+ 0.41294967113388825,
+ 0.40882017442254937,
+ 0.4047319726783239,
+ 0.40068465295154065,
+ 0.39667780642202527,
+ 0.392711028357805,
+ 0.38878391807422696,
+ 0.3848960788934847,
+ 0.38104711810454983,
+ 0.37723664692350434,
+ 0.37346428045426927,
+ 0.36972963764972655,
+ 0.36603234127322926,
+ 0.36237201786049694,
+ 0.358748297681892,
+ 0.35516081470507305,
+ 0.3516092065580223,
+ 0.34809311449244207,
+ 0.34461218334751764,
+ 0.34116606151404244,
+ 0.337754400898902,
+ 0.334376856889913,
+ 0.33103308832101386,
+ 0.3277227574378037,
+ 0.3244455298634257,
+ 0.3212010745647914,
+ 0.3179890638191435,
+ 0.31480917318095203,
+ 0.3116610814491425,
+ 0.30854447063465107,
+ 0.30545902592830454,
+ 0.3024044356690215,
+ 0.29938039131233124,
+ 0.2963865873992079,
+ 0.29342272152521587,
+ 0.2904884943099637,
+ 0.28758360936686406,
+ 0.2847077732731954,
+ 0.28186069554046345,
+ 0.2790420885850588,
+ 0.2762516676992082,
+ 0.27348915102221616,
+ 0.270754259511994,
+ 0.26804671691687404,
+ 0.2653662497477053,
+ 0.2627125872502282,
+ 0.2600854613777259,
+ 0.2574846067639487,
+ 0.2549097606963092,
+ 0.2523606630893461,
+ 0.24983705645845267,
+ 0.24733868589386815,
+ 0.24486529903492943,
+ 0.24241664604458016,
+ 0.23999247958413436,
+ 0.23759255478829305,
+ 0.2352166292404101,
+ 0.232864462948006,
+ 0.23053581831852593,
+ 0.22823046013534068,
+ 0.22594815553398728,
+ 0.22368867397864745,
+ 0.22145178723886097,
+ 0.2192372693664723,
+ 0.2170448966728076,
+ 0.21487444770607952,
+ 0.2127257032290187,
+ 0.21059844619672857,
+ 0.20849246173476127,
+ 0.2064075371174137,
+ 0.20434346174623952,
+ 0.20230002712877712,
+ 0.20027702685748935,
+ 0.19827425658891443,
+ 0.1962915140230253,
+ 0.19432859888279505,
+ 0.1923853128939671,
+ 0.19046145976502743,
+ 0.1885568451673771,
+ 0.18667127671570335,
+ 0.18480456394854633,
+ 0.18295651830906087,
+ 0.18112695312597027,
+ 0.17931568359471056,
+ 0.17752252675876343,
+ 0.17574730149117582,
+ 0.17398982847626407,
+ 0.17224993019150142,
+ 0.1705274308895864,
+ 0.16882215658069055,
+ 0.16713393501488363,
+ 0.1654625956647348,
+ 0.16380796970808745,
+ 0.16216989001100657,
+ 0.1605481911108965,
+ 0.15894270919978754,
+ 0.15735328210778965,
+ 0.15577974928671176,
+ 0.15422195179384465,
+ 0.1526797322759062,
+ 0.15115293495314713,
+ 0.14964140560361566,
+ 0.1481449915475795,
+ 0.1466635416321037,
+ 0.14519690621578268,
+ 0.14374493715362485,
+ 0.1423074877820886,
+ 0.1408844129042677,
+ 0.13947556877522502,
+ 0.13808081308747278,
+ 0.13670000495659804,
+ 0.13533300490703207,
+ 0.13397967485796175,
+ 0.13263987810938213,
+ 0.1313134793282883,
+ 0.13000034453500542,
+ 0.12870034108965536,
+ 0.1274133376787588,
+ 0.12613920430197123,
+ 0.12487781225895152,
+ 0.123629034136362,
+ 0.12239274379499838,
+ 0.1211688163570484,
+ 0.11995712819347792,
+ 0.11875755691154316,
+ 0.11756998134242772,
+ 0.11639428152900344,
+ 0.1152303387137134,
+ 0.11407803532657627,
+ 0.11293725497331052,
+ 0.1118078824235774,
+ 0.11068980359934164,
+ 0.10958290556334822,
+ 0.10848707650771476,
+ 0.1074022057426376,
+ 0.10632818368521124,
+ 0.10526490184835911,
+ 0.10421225282987552,
+ 0.10317013030157676,
+ 0.102138428998561,
+ 0.10111704470857538,
+ 0.10010587426148965,
+ 0.09910481551887472,
+ 0.098113767363686,
+ 0.09713262969004911,
+ 0.09616130339314864,
+ 0.09519969035921716,
+ 0.09424769345562498,
+ 0.09330521652106873,
+ 0.09237216435585804,
+ 0.09144844271229946,
+ 0.09053395828517646,
+ 0.08962861870232469,
+ 0.08873233251530144,
+ 0.08784500919014843,
+ 0.08696655909824694,
+ 0.08609689350726446,
+ 0.08523592457219181,
+ 0.0843835653264699,
+ 0.0835397296732052,
+ 0.08270433237647315,
+ 0.08187728905270841,
+ 0.08105851616218133,
+ 0.08024793100055952,
+ 0.07944545169055392,
+ 0.07865099717364837,
+ 0.07786448720191189,
+ 0.07708584232989277,
+ 0.07631498390659384,
+ 0.07555183406752791,
+ 0.07479631572685264,
+ 0.07404835256958411,
+ 0.07330786904388827,
+ 0.07257479035344938,
+ 0.07184904244991488,
+ 0.07113055202541574,
+ 0.07041924650516158,
+ 0.06971505404010997,
+ 0.06901790349970886,
+ 0.06832772446471178,
+ 0.06764444722006466,
+ 0.066968002747864,
+ 0.06629832272038537,
+ 0.06563533949318151,
+ 0.06497898609824969,
+ 0.0643291962372672,
+ 0.06368590427489453,
+ 0.06304904523214558,
+ 0.06241855477982412,
+ 0.06179436923202588,
+ 0.06117642553970562,
+ 0.06056466128430856,
+ 0.05995901467146548,
+ 0.05935942452475082,
+ 0.058765830279503314,
+ 0.05817817197670828,
+ 0.057596390256941195,
+ 0.05702042635437178,
+ 0.05645022209082806,
+ 0.05588571986991978,
+ 0.055326862671220584,
+ 0.05477359404450838,
+ 0.054225858104063294,
+ 0.05368359952302266,
+ 0.053146763527792434,
+ 0.052615295892514506,
+ 0.05208914293358936,
+ 0.05156825150425347,
+ 0.051052568989210935,
+ 0.05054204329931883,
+ 0.05003662286632564,
+ 0.04953625663766238,
+ 0.04904089407128576,
+ 0.0485504851305729,
+ 0.048064980279267165,
+ 0.04758433047647449,
+ 0.04710848717170975,
+ 0.04663740229999265,
+ 0.04617102827699272,
+ 0.045709317994222794,
+ 0.04525222481428057,
+ 0.04479970256613776,
+ 0.04435170554047638,
+ 0.043908188485071616,
+ 0.0434691066002209,
+ 0.04303441553421869,
+ 0.0426040713788765,
+ 0.04217803066508773,
+ 0.04175625035843686,
+ 0.041338687854852486,
+ 0.04092530097630396,
+ 0.04051604796654091,
+ 0.04011088748687551,
+ 0.03970977861200675,
+ 0.03931268082588668,
+ 0.03891955401762781,
+ 0.03853035847745153,
+ 0.03814505489267701,
+ 0.03776360434375024,
+ 0.03738596830031274,
+ 0.03701210861730961,
+ 0.03664198753113651,
+ 0.036275567655825146,
+ 0.03591281197926689,
+ 0.035553683859474224,
+ 0.03519814702087948,
+ 0.03484616555067068,
+ 0.034497703895163975,
+ 0.03415272685621234,
+ 0.03381119958765021,
+ 0.03347308759177371,
+ 0.03313835671585597,
+ 0.03280697314869741,
+ 0.032478903417210436,
+ 0.032154114383038335,
+ 0.03183257323920795,
+ 0.03151424750681587,
+ 0.03119910503174771,
+ 0.030887113981430233,
+ 0.03057824284161593,
+ 0.03027246041319977,
+ 0.029969735809067772,
+ 0.029670038450977095,
+ 0.029373338066467324,
+ 0.02907960468580265,
+ 0.028788808638944625,
+ 0.028500920552555178,
+ 0.028215911347029624,
+ 0.027933752233559327,
+ 0.027654414711223735,
+ 0.027377870564111496,
+ 0.027104091858470385,
+ 0.026833050939885677,
+ 0.02656472043048682,
+ 0.02629907322618195,
+ 0.026036082493920133,
+ 0.02577572166898093,
+ 0.02551796445229112,
+ 0.02526278480776821,
+ 0.025010156959690527,
+ 0.02476005539009362,
+ 0.024512454836192684,
+ 0.024267330287830756,
+ 0.024024656984952448,
+ 0.023784410415102923,
+ 0.023546566310951898,
+ 0.023311100647842375,
+ 0.02307798964136395,
+ 0.022847209744950314,
+ 0.02261873764750081,
+ 0.022392550271025803,
+ 0.022168624768315544,
+ 0.02194693852063239,
+ 0.021727469135426065,
+ 0.021510194444071803,
+ 0.021295092499631085,
+ 0.021082141574634772,
+ 0.020871320158888425,
+ 0.020662606957299545,
+ 0.020455980887726547,
+ 0.02025142107884928,
+ 0.020048906868060788,
+ 0.01984841779938018,
+ 0.019649933621386374,
+ 0.019453434285172513,
+ 0.019258899942320787,
+ 0.01906631094289758,
+ 0.0188756478334686,
+ 0.018686891355133916,
+ 0.018500022441582577,
+ 0.01831502221716675,
+ 0.018131871994995084,
+ 0.017950553275045134,
+ 0.017771047742294682,
+ 0.017593337264871736,
+ 0.01741740389222302,
+ 0.01724322985330079,
+ 0.017070797554767782,
+ 0.016900089579220106,
+ 0.016731088683427906,
+ 0.016563777796593626,
+ 0.016398140018627688,
+ 0.01623415861844141,
+ 0.016071817032256998,
+ 0.01591109886193443,
+ 0.015751987873315085,
+ 0.015594467994581937,
+ 0.015438523314636117,
+ 0.015284138081489752,
+ 0.015131296700674856,
+ 0.014979983733668108,
+ 0.014830183896331426,
+ 0.014681882057368112,
+ 0.01453506323679443,
+ 0.014389712604426484,
+ 0.01424581547838222,
+ 0.014103357323598397,
+ 0.013962323750362412,
+ 0.013822700512858789,
+ 0.0136844735077302,
+ 0.0135476287726529,
+ 0.01341215248492637,
+ 0.013278030960077106,
+ 0.013145250650476337,
+ 0.01301379814397157,
+ 0.012883660162531854,
+ 0.012754823560906537,
+ 0.01262727532529747,
+ 0.012501002572044496,
+ 0.01237599254632405,
+ 0.01225223262086081,
+ 0.012129710294652202,
+ 0.01200841319170568,
+ 0.011888329059788623,
+ 0.011769445769190735,
+ 0.01165175131149883,
+ 0.011535233798383842,
+ 0.011419881460400004,
+ 0.011305682645796004,
+ 0.011192625819338045,
+ 0.011080699561144665,
+ 0.010969892565533218,
+ 0.010860193639877886,
+ 0.010751591703479106,
+ 0.010644075786444315,
+ 0.010537635028579871,
+ 0.010432258678294072,
+ 0.010327936091511131,
+ 0.010224656730596022,
+ 0.01012241016329006,
+ 0.01002118606165716,
+ 0.009920974201040588,
+ 0.009821764459030182,
+ 0.00972354681443988,
+ 0.00962631134629548,
+ 0.009530048232832523,
+ 0.0094347477505042,
+ 0.009340400272999157,
+ 0.009246996270269163,
+ 0.009154526307566474,
+ 0.009062981044490808,
+ 0.0089723512340459,
+ 0.00888262772170544,
+ 0.008793801444488386,
+ 0.008705863430043502,
+ 0.008618804795743068,
+ 0.008532616747785637,
+ 0.00844729058030778,
+ 0.008362817674504702,
+ 0.008279189497759654,
+ 0.008196397602782058,
+ 0.008114433626754238,
+ 0.008033289290486696,
+ 0.00795295639758183,
+ 0.00787342683360601,
+ 0.007794692565269949,
+ 0.00771674563961725,
+ 0.007639578183221077,
+ 0.007563182401388867,
+ 0.007487550577374978,
+ 0.007412675071601228,
+ 0.007338548320885215,
+ 0.0072651628376763635,
+ 0.0071925112092996,
+ 0.007120586097206604,
+ 0.007049380236234538,
+ 0.006978886433872193,
+ 0.006909097569533471,
+ 0.006840006593838137,
+ 0.0067716065278997555,
+ 0.006703890462620758,
+ 0.006636851557994551
+ ],
+ "yaxis": "y2"
+ },
+ {
+ "mode": "lines",
+ "name": "Score",
+ "type": "scatter",
+ "x": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 60,
+ 61,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 81,
+ 82,
+ 83,
+ 84,
+ 85,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 91,
+ 92,
+ 93,
+ 94,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 108,
+ 109,
+ 110,
+ 111,
+ 112,
+ 113,
+ 114,
+ 115,
+ 116,
+ 117,
+ 118,
+ 119,
+ 120,
+ 121,
+ 122,
+ 123,
+ 124,
+ 125,
+ 126,
+ 127,
+ 128,
+ 129,
+ 130,
+ 131,
+ 132,
+ 133,
+ 134,
+ 135,
+ 136,
+ 137,
+ 138,
+ 139,
+ 140,
+ 141,
+ 142,
+ 143,
+ 144,
+ 145,
+ 146,
+ 147,
+ 148,
+ 149,
+ 150,
+ 151,
+ 152,
+ 153,
+ 154,
+ 155,
+ 156,
+ 157,
+ 158,
+ 159,
+ 160,
+ 161,
+ 162,
+ 163,
+ 164,
+ 165,
+ 166,
+ 167,
+ 168,
+ 169,
+ 170,
+ 171,
+ 172,
+ 173,
+ 174,
+ 175,
+ 176,
+ 177,
+ 178,
+ 179,
+ 180,
+ 181,
+ 182,
+ 183,
+ 184,
+ 185,
+ 186,
+ 187,
+ 188,
+ 189,
+ 190,
+ 191,
+ 192,
+ 193,
+ 194,
+ 195,
+ 196,
+ 197,
+ 198,
+ 199,
+ 200,
+ 201,
+ 202,
+ 203,
+ 204,
+ 205,
+ 206,
+ 207,
+ 208,
+ 209,
+ 210,
+ 211,
+ 212,
+ 213,
+ 214,
+ 215,
+ 216,
+ 217,
+ 218,
+ 219,
+ 220,
+ 221,
+ 222,
+ 223,
+ 224,
+ 225,
+ 226,
+ 227,
+ 228,
+ 229,
+ 230,
+ 231,
+ 232,
+ 233,
+ 234,
+ 235,
+ 236,
+ 237,
+ 238,
+ 239,
+ 240,
+ 241,
+ 242,
+ 243,
+ 244,
+ 245,
+ 246,
+ 247,
+ 248,
+ 249,
+ 250,
+ 251,
+ 252,
+ 253,
+ 254,
+ 255,
+ 256,
+ 257,
+ 258,
+ 259,
+ 260,
+ 261,
+ 262,
+ 263,
+ 264,
+ 265,
+ 266,
+ 267,
+ 268,
+ 269,
+ 270,
+ 271,
+ 272,
+ 273,
+ 274,
+ 275,
+ 276,
+ 277,
+ 278,
+ 279,
+ 280,
+ 281,
+ 282,
+ 283,
+ 284,
+ 285,
+ 286,
+ 287,
+ 288,
+ 289,
+ 290,
+ 291,
+ 292,
+ 293,
+ 294,
+ 295,
+ 296,
+ 297,
+ 298,
+ 299,
+ 300,
+ 301,
+ 302,
+ 303,
+ 304,
+ 305,
+ 306,
+ 307,
+ 308,
+ 309,
+ 310,
+ 311,
+ 312,
+ 313,
+ 314,
+ 315,
+ 316,
+ 317,
+ 318,
+ 319,
+ 320,
+ 321,
+ 322,
+ 323,
+ 324,
+ 325,
+ 326,
+ 327,
+ 328,
+ 329,
+ 330,
+ 331,
+ 332,
+ 333,
+ 334,
+ 335,
+ 336,
+ 337,
+ 338,
+ 339,
+ 340,
+ 341,
+ 342,
+ 343,
+ 344,
+ 345,
+ 346,
+ 347,
+ 348,
+ 349,
+ 350,
+ 351,
+ 352,
+ 353,
+ 354,
+ 355,
+ 356,
+ 357,
+ 358,
+ 359,
+ 360,
+ 361,
+ 362,
+ 363,
+ 364,
+ 365,
+ 366,
+ 367,
+ 368,
+ 369,
+ 370,
+ 371,
+ 372,
+ 373,
+ 374,
+ 375,
+ 376,
+ 377,
+ 378,
+ 379,
+ 380,
+ 381,
+ 382,
+ 383,
+ 384,
+ 385,
+ 386,
+ 387,
+ 388,
+ 389,
+ 390,
+ 391,
+ 392,
+ 393,
+ 394,
+ 395,
+ 396,
+ 397,
+ 398,
+ 399,
+ 400,
+ 401,
+ 402,
+ 403,
+ 404,
+ 405,
+ 406,
+ 407,
+ 408,
+ 409,
+ 410,
+ 411,
+ 412,
+ 413,
+ 414,
+ 415,
+ 416,
+ 417,
+ 418,
+ 419,
+ 420,
+ 421,
+ 422,
+ 423,
+ 424,
+ 425,
+ 426,
+ 427,
+ 428,
+ 429,
+ 430,
+ 431,
+ 432,
+ 433,
+ 434,
+ 435,
+ 436,
+ 437,
+ 438,
+ 439,
+ 440,
+ 441,
+ 442,
+ 443,
+ 444,
+ 445,
+ 446,
+ 447,
+ 448,
+ 449,
+ 450,
+ 451,
+ 452,
+ 453,
+ 454,
+ 455,
+ 456,
+ 457,
+ 458,
+ 459,
+ 460,
+ 461,
+ 462,
+ 463,
+ 464,
+ 465,
+ 466,
+ 467,
+ 468,
+ 469,
+ 470,
+ 471,
+ 472,
+ 473,
+ 474,
+ 475,
+ 476,
+ 477,
+ 478,
+ 479,
+ 480,
+ 481,
+ 482,
+ 483,
+ 484,
+ 485,
+ 486,
+ 487,
+ 488,
+ 489,
+ 490,
+ 491,
+ 492,
+ 493,
+ 494,
+ 495,
+ 496,
+ 497,
+ 498,
+ 499
+ ],
+ "xaxis": "x",
+ "y": [
+ 11,
+ 13,
+ 17,
+ 14,
+ 21,
+ 17,
+ 13,
+ 39,
+ 15,
+ 10,
+ 15,
+ 22,
+ 17,
+ 21,
+ 19,
+ 38,
+ 12,
+ 16,
+ 11,
+ 15,
+ 18,
+ 21,
+ 24,
+ 17,
+ 14,
+ 27,
+ 18,
+ 24,
+ 39,
+ 30,
+ 57,
+ 33,
+ 17,
+ 15,
+ 32,
+ 32,
+ 77,
+ 26,
+ 14,
+ 32,
+ 35,
+ 14,
+ 22,
+ 53,
+ 39,
+ 46,
+ 12,
+ 62,
+ 81,
+ 46,
+ 87,
+ 15,
+ 22,
+ 59,
+ 26,
+ 44,
+ 63,
+ 16,
+ 61,
+ 71,
+ 13,
+ 55,
+ 49,
+ 100,
+ 28,
+ 19,
+ 31,
+ 59,
+ 13,
+ 29,
+ 51,
+ 53,
+ 61,
+ 71,
+ 27,
+ 61,
+ 93,
+ 74,
+ 86,
+ 63,
+ 98,
+ 43,
+ 86,
+ 100,
+ 51,
+ 97,
+ 100,
+ 100,
+ 65,
+ 31,
+ 53,
+ 69,
+ 100,
+ 35,
+ 96,
+ 100,
+ 39,
+ 60,
+ 100,
+ 100,
+ 100,
+ 61,
+ 62,
+ 82,
+ 100,
+ 97,
+ 72,
+ 65,
+ 31,
+ 100,
+ 68,
+ 51,
+ 100,
+ 37,
+ 100,
+ 100,
+ 99,
+ 100,
+ 50,
+ 100,
+ 100,
+ 100,
+ 100,
+ 84,
+ 95,
+ 74,
+ 78,
+ 80,
+ 100,
+ 25,
+ 70,
+ 83,
+ 100,
+ 48,
+ 100,
+ 57,
+ 100,
+ 100,
+ 61,
+ 65,
+ 100,
+ 47,
+ 100,
+ 100,
+ 100,
+ 100,
+ 73,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 41,
+ 84,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 80,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 86,
+ 55,
+ 100,
+ 100,
+ 71,
+ 87,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 84,
+ 100,
+ 54,
+ 85,
+ 100,
+ 100,
+ 95,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 38,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 82,
+ 94,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 96,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 67,
+ 100,
+ 100,
+ 100,
+ 53,
+ 100,
+ 85,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100
+ ],
+ "yaxis": "y"
+ }
+ ],
+ "layout": {
+ "template": {
+ "data": {
+ "bar": [
+ {
+ "error_x": {
+ "color": "#2a3f5f"
+ },
+ "error_y": {
+ "color": "#2a3f5f"
+ },
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "bar"
+ }
+ ],
+ "barpolar": [
+ {
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "barpolar"
+ }
+ ],
+ "carpet": [
+ {
+ "aaxis": {
+ "endlinecolor": "#2a3f5f",
+ "gridcolor": "white",
+ "linecolor": "white",
+ "minorgridcolor": "white",
+ "startlinecolor": "#2a3f5f"
+ },
+ "baxis": {
+ "endlinecolor": "#2a3f5f",
+ "gridcolor": "white",
+ "linecolor": "white",
+ "minorgridcolor": "white",
+ "startlinecolor": "#2a3f5f"
+ },
+ "type": "carpet"
+ }
+ ],
+ "choropleth": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "choropleth"
+ }
+ ],
+ "contour": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "contour"
+ }
+ ],
+ "contourcarpet": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "contourcarpet"
+ }
+ ],
+ "heatmap": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "heatmap"
+ }
+ ],
+ "heatmapgl": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "heatmapgl"
+ }
+ ],
+ "histogram": [
+ {
+ "marker": {
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "histogram"
+ }
+ ],
+ "histogram2d": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "histogram2d"
+ }
+ ],
+ "histogram2dcontour": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "histogram2dcontour"
+ }
+ ],
+ "mesh3d": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "mesh3d"
+ }
+ ],
+ "parcoords": [
+ {
+ "line": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "parcoords"
+ }
+ ],
+ "pie": [
+ {
+ "automargin": true,
+ "type": "pie"
+ }
+ ],
+ "scatter": [
+ {
+ "fillpattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ },
+ "type": "scatter"
+ }
+ ],
+ "scatter3d": [
+ {
+ "line": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatter3d"
+ }
+ ],
+ "scattercarpet": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattercarpet"
+ }
+ ],
+ "scattergeo": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattergeo"
+ }
+ ],
+ "scattergl": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattergl"
+ }
+ ],
+ "scattermapbox": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattermapbox"
+ }
+ ],
+ "scatterpolar": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterpolar"
+ }
+ ],
+ "scatterpolargl": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterpolargl"
+ }
+ ],
+ "scatterternary": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterternary"
+ }
+ ],
+ "surface": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "surface"
+ }
+ ],
+ "table": [
+ {
+ "cells": {
+ "fill": {
+ "color": "#EBF0F8"
+ },
+ "line": {
+ "color": "white"
+ }
+ },
+ "header": {
+ "fill": {
+ "color": "#C8D4E3"
+ },
+ "line": {
+ "color": "white"
+ }
+ },
+ "type": "table"
+ }
+ ]
+ },
+ "layout": {
+ "annotationdefaults": {
+ "arrowcolor": "#2a3f5f",
+ "arrowhead": 0,
+ "arrowwidth": 1
+ },
+ "autotypenumbers": "strict",
+ "coloraxis": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "colorscale": {
+ "diverging": [
+ [
+ 0,
+ "#8e0152"
+ ],
+ [
+ 0.1,
+ "#c51b7d"
+ ],
+ [
+ 0.2,
+ "#de77ae"
+ ],
+ [
+ 0.3,
+ "#f1b6da"
+ ],
+ [
+ 0.4,
+ "#fde0ef"
+ ],
+ [
+ 0.5,
+ "#f7f7f7"
+ ],
+ [
+ 0.6,
+ "#e6f5d0"
+ ],
+ [
+ 0.7,
+ "#b8e186"
+ ],
+ [
+ 0.8,
+ "#7fbc41"
+ ],
+ [
+ 0.9,
+ "#4d9221"
+ ],
+ [
+ 1,
+ "#276419"
+ ]
+ ],
+ "sequential": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "sequentialminus": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ]
+ },
+ "colorway": [
+ "#636efa",
+ "#EF553B",
+ "#00cc96",
+ "#ab63fa",
+ "#FFA15A",
+ "#19d3f3",
+ "#FF6692",
+ "#B6E880",
+ "#FF97FF",
+ "#FECB52"
+ ],
+ "font": {
+ "color": "#2a3f5f"
+ },
+ "geo": {
+ "bgcolor": "white",
+ "lakecolor": "white",
+ "landcolor": "#E5ECF6",
+ "showlakes": true,
+ "showland": true,
+ "subunitcolor": "white"
+ },
+ "hoverlabel": {
+ "align": "left"
+ },
+ "hovermode": "closest",
+ "mapbox": {
+ "style": "light"
+ },
+ "paper_bgcolor": "white",
+ "plot_bgcolor": "#E5ECF6",
+ "polar": {
+ "angularaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "radialaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "scene": {
+ "xaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "yaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "zaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "caxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "DDQN Training, Advantage Type: AdvantageType.AVG"
+ },
+ "xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 0.94
+ ],
+ "title": {
+ "text": "Episode"
+ }
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Score"
+ }
+ },
+ "yaxis2": {
+ "anchor": "x",
+ "overlaying": "y",
+ "side": "right",
+ "title": {
+ "text": "Exploration Rate"
+ }
+ }
+ }
+ },
+ "image/png": "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",
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "20ad86ae8b914f189dac61b5ee9383f2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/500 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/ryan.peach/Documents/ryanpeach/continuing_education/.venv/lib/python3.11/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning:\n",
+ "\n",
+ "`np.bool8` is a deprecated alias for `np.bool_`. (Deprecated NumPy 1.24)\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "mode": "lines",
+ "name": "Exploration Rate",
+ "type": "scatter",
+ "x": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 60,
+ 61,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 81,
+ 82,
+ 83,
+ 84,
+ 85,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 91,
+ 92,
+ 93,
+ 94,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 108,
+ 109,
+ 110,
+ 111,
+ 112,
+ 113,
+ 114,
+ 115,
+ 116,
+ 117,
+ 118,
+ 119,
+ 120,
+ 121,
+ 122,
+ 123,
+ 124,
+ 125,
+ 126,
+ 127,
+ 128,
+ 129,
+ 130,
+ 131,
+ 132,
+ 133,
+ 134,
+ 135,
+ 136,
+ 137,
+ 138,
+ 139,
+ 140,
+ 141,
+ 142,
+ 143,
+ 144,
+ 145,
+ 146,
+ 147,
+ 148,
+ 149,
+ 150,
+ 151,
+ 152,
+ 153,
+ 154,
+ 155,
+ 156,
+ 157,
+ 158,
+ 159,
+ 160,
+ 161,
+ 162,
+ 163,
+ 164,
+ 165,
+ 166,
+ 167,
+ 168,
+ 169,
+ 170,
+ 171,
+ 172,
+ 173,
+ 174,
+ 175,
+ 176,
+ 177,
+ 178,
+ 179,
+ 180,
+ 181,
+ 182,
+ 183,
+ 184,
+ 185,
+ 186,
+ 187,
+ 188,
+ 189,
+ 190,
+ 191,
+ 192,
+ 193,
+ 194,
+ 195,
+ 196,
+ 197,
+ 198,
+ 199,
+ 200,
+ 201,
+ 202,
+ 203,
+ 204,
+ 205,
+ 206,
+ 207,
+ 208,
+ 209,
+ 210,
+ 211,
+ 212,
+ 213,
+ 214,
+ 215,
+ 216,
+ 217,
+ 218,
+ 219,
+ 220,
+ 221,
+ 222,
+ 223,
+ 224,
+ 225,
+ 226,
+ 227,
+ 228,
+ 229,
+ 230,
+ 231,
+ 232,
+ 233,
+ 234,
+ 235,
+ 236,
+ 237,
+ 238,
+ 239,
+ 240,
+ 241,
+ 242,
+ 243,
+ 244,
+ 245,
+ 246,
+ 247,
+ 248,
+ 249,
+ 250,
+ 251,
+ 252,
+ 253,
+ 254,
+ 255,
+ 256,
+ 257,
+ 258,
+ 259,
+ 260,
+ 261,
+ 262,
+ 263,
+ 264,
+ 265,
+ 266,
+ 267,
+ 268,
+ 269,
+ 270,
+ 271,
+ 272,
+ 273,
+ 274,
+ 275,
+ 276,
+ 277,
+ 278,
+ 279,
+ 280,
+ 281,
+ 282,
+ 283,
+ 284,
+ 285,
+ 286,
+ 287,
+ 288,
+ 289,
+ 290,
+ 291,
+ 292,
+ 293,
+ 294,
+ 295,
+ 296,
+ 297,
+ 298,
+ 299,
+ 300,
+ 301,
+ 302,
+ 303,
+ 304,
+ 305,
+ 306,
+ 307,
+ 308,
+ 309,
+ 310,
+ 311,
+ 312,
+ 313,
+ 314,
+ 315,
+ 316,
+ 317,
+ 318,
+ 319,
+ 320,
+ 321,
+ 322,
+ 323,
+ 324,
+ 325,
+ 326,
+ 327,
+ 328,
+ 329,
+ 330,
+ 331,
+ 332,
+ 333,
+ 334,
+ 335,
+ 336,
+ 337,
+ 338,
+ 339,
+ 340,
+ 341,
+ 342,
+ 343,
+ 344,
+ 345,
+ 346,
+ 347,
+ 348,
+ 349,
+ 350,
+ 351,
+ 352,
+ 353,
+ 354,
+ 355,
+ 356,
+ 357,
+ 358,
+ 359,
+ 360,
+ 361,
+ 362,
+ 363,
+ 364,
+ 365,
+ 366,
+ 367,
+ 368,
+ 369,
+ 370,
+ 371,
+ 372,
+ 373,
+ 374,
+ 375,
+ 376,
+ 377,
+ 378,
+ 379,
+ 380,
+ 381,
+ 382,
+ 383,
+ 384,
+ 385,
+ 386,
+ 387,
+ 388,
+ 389,
+ 390,
+ 391,
+ 392,
+ 393,
+ 394,
+ 395,
+ 396,
+ 397,
+ 398,
+ 399,
+ 400,
+ 401,
+ 402,
+ 403,
+ 404,
+ 405,
+ 406,
+ 407,
+ 408,
+ 409,
+ 410,
+ 411,
+ 412,
+ 413,
+ 414,
+ 415,
+ 416,
+ 417,
+ 418,
+ 419,
+ 420,
+ 421,
+ 422,
+ 423,
+ 424,
+ 425,
+ 426,
+ 427,
+ 428,
+ 429,
+ 430,
+ 431,
+ 432,
+ 433,
+ 434,
+ 435,
+ 436,
+ 437,
+ 438,
+ 439,
+ 440,
+ 441,
+ 442,
+ 443,
+ 444,
+ 445,
+ 446,
+ 447,
+ 448,
+ 449,
+ 450,
+ 451,
+ 452,
+ 453,
+ 454,
+ 455,
+ 456,
+ 457,
+ 458,
+ 459,
+ 460,
+ 461,
+ 462,
+ 463,
+ 464,
+ 465,
+ 466,
+ 467,
+ 468,
+ 469,
+ 470,
+ 471,
+ 472,
+ 473,
+ 474,
+ 475,
+ 476,
+ 477,
+ 478,
+ 479,
+ 480,
+ 481,
+ 482,
+ 483,
+ 484,
+ 485,
+ 486,
+ 487,
+ 488,
+ 489,
+ 490,
+ 491,
+ 492,
+ 493,
+ 494,
+ 495,
+ 496,
+ 497,
+ 498,
+ 499
+ ],
+ "xaxis": "x",
+ "y": [
+ 1,
+ 0.99,
+ 0.9801,
+ 0.970299,
+ 0.96059601,
+ 0.9509900499,
+ 0.941480149401,
+ 0.93206534790699,
+ 0.92274469442792,
+ 0.9135172474836408,
+ 0.9043820750088044,
+ 0.8953382542587163,
+ 0.8863848717161291,
+ 0.8775210229989678,
+ 0.8687458127689781,
+ 0.8600583546412883,
+ 0.8514577710948754,
+ 0.8429431933839266,
+ 0.8345137614500874,
+ 0.8261686238355865,
+ 0.8179069375972307,
+ 0.8097278682212583,
+ 0.8016305895390458,
+ 0.7936142836436553,
+ 0.7856781408072188,
+ 0.7778213593991465,
+ 0.7700431458051551,
+ 0.7623427143471035,
+ 0.7547192872036325,
+ 0.7471720943315961,
+ 0.7397003733882802,
+ 0.7323033696543974,
+ 0.7249803359578534,
+ 0.7177305325982748,
+ 0.7105532272722921,
+ 0.7034476949995692,
+ 0.6964132180495735,
+ 0.6894490858690777,
+ 0.682554595010387,
+ 0.6757290490602831,
+ 0.6689717585696803,
+ 0.6622820409839835,
+ 0.6556592205741436,
+ 0.6491026283684022,
+ 0.6426116020847181,
+ 0.6361854860638709,
+ 0.6298236312032323,
+ 0.6235253948912,
+ 0.617290140942288,
+ 0.6111172395328651,
+ 0.6050060671375365,
+ 0.5989560064661611,
+ 0.5929664464014994,
+ 0.5870367819374844,
+ 0.5811664141181095,
+ 0.5753547499769285,
+ 0.5696012024771592,
+ 0.5639051904523876,
+ 0.5582661385478638,
+ 0.5526834771623851,
+ 0.5471566423907612,
+ 0.5416850759668536,
+ 0.536268225207185,
+ 0.5309055429551132,
+ 0.525596487525562,
+ 0.5203405226503064,
+ 0.5151371174238033,
+ 0.5099857462495653,
+ 0.5048858887870696,
+ 0.4998370298991989,
+ 0.49483865960020695,
+ 0.4898902730042049,
+ 0.4849913702741629,
+ 0.4801414565714212,
+ 0.475340042005707,
+ 0.47058664158564995,
+ 0.4658807751697934,
+ 0.4612219674180955,
+ 0.45660974774391455,
+ 0.4520436502664754,
+ 0.44752321376381066,
+ 0.44304798162617254,
+ 0.4386175018099108,
+ 0.4342313267918117,
+ 0.4298890135238936,
+ 0.42559012338865465,
+ 0.4213342221547681,
+ 0.4171208799332205,
+ 0.41294967113388825,
+ 0.40882017442254937,
+ 0.4047319726783239,
+ 0.40068465295154065,
+ 0.39667780642202527,
+ 0.392711028357805,
+ 0.38878391807422696,
+ 0.3848960788934847,
+ 0.38104711810454983,
+ 0.37723664692350434,
+ 0.37346428045426927,
+ 0.36972963764972655,
+ 0.36603234127322926,
+ 0.36237201786049694,
+ 0.358748297681892,
+ 0.35516081470507305,
+ 0.3516092065580223,
+ 0.34809311449244207,
+ 0.34461218334751764,
+ 0.34116606151404244,
+ 0.337754400898902,
+ 0.334376856889913,
+ 0.33103308832101386,
+ 0.3277227574378037,
+ 0.3244455298634257,
+ 0.3212010745647914,
+ 0.3179890638191435,
+ 0.31480917318095203,
+ 0.3116610814491425,
+ 0.30854447063465107,
+ 0.30545902592830454,
+ 0.3024044356690215,
+ 0.29938039131233124,
+ 0.2963865873992079,
+ 0.29342272152521587,
+ 0.2904884943099637,
+ 0.28758360936686406,
+ 0.2847077732731954,
+ 0.28186069554046345,
+ 0.2790420885850588,
+ 0.2762516676992082,
+ 0.27348915102221616,
+ 0.270754259511994,
+ 0.26804671691687404,
+ 0.2653662497477053,
+ 0.2627125872502282,
+ 0.2600854613777259,
+ 0.2574846067639487,
+ 0.2549097606963092,
+ 0.2523606630893461,
+ 0.24983705645845267,
+ 0.24733868589386815,
+ 0.24486529903492943,
+ 0.24241664604458016,
+ 0.23999247958413436,
+ 0.23759255478829305,
+ 0.2352166292404101,
+ 0.232864462948006,
+ 0.23053581831852593,
+ 0.22823046013534068,
+ 0.22594815553398728,
+ 0.22368867397864745,
+ 0.22145178723886097,
+ 0.2192372693664723,
+ 0.2170448966728076,
+ 0.21487444770607952,
+ 0.2127257032290187,
+ 0.21059844619672857,
+ 0.20849246173476127,
+ 0.2064075371174137,
+ 0.20434346174623952,
+ 0.20230002712877712,
+ 0.20027702685748935,
+ 0.19827425658891443,
+ 0.1962915140230253,
+ 0.19432859888279505,
+ 0.1923853128939671,
+ 0.19046145976502743,
+ 0.1885568451673771,
+ 0.18667127671570335,
+ 0.18480456394854633,
+ 0.18295651830906087,
+ 0.18112695312597027,
+ 0.17931568359471056,
+ 0.17752252675876343,
+ 0.17574730149117582,
+ 0.17398982847626407,
+ 0.17224993019150142,
+ 0.1705274308895864,
+ 0.16882215658069055,
+ 0.16713393501488363,
+ 0.1654625956647348,
+ 0.16380796970808745,
+ 0.16216989001100657,
+ 0.1605481911108965,
+ 0.15894270919978754,
+ 0.15735328210778965,
+ 0.15577974928671176,
+ 0.15422195179384465,
+ 0.1526797322759062,
+ 0.15115293495314713,
+ 0.14964140560361566,
+ 0.1481449915475795,
+ 0.1466635416321037,
+ 0.14519690621578268,
+ 0.14374493715362485,
+ 0.1423074877820886,
+ 0.1408844129042677,
+ 0.13947556877522502,
+ 0.13808081308747278,
+ 0.13670000495659804,
+ 0.13533300490703207,
+ 0.13397967485796175,
+ 0.13263987810938213,
+ 0.1313134793282883,
+ 0.13000034453500542,
+ 0.12870034108965536,
+ 0.1274133376787588,
+ 0.12613920430197123,
+ 0.12487781225895152,
+ 0.123629034136362,
+ 0.12239274379499838,
+ 0.1211688163570484,
+ 0.11995712819347792,
+ 0.11875755691154316,
+ 0.11756998134242772,
+ 0.11639428152900344,
+ 0.1152303387137134,
+ 0.11407803532657627,
+ 0.11293725497331052,
+ 0.1118078824235774,
+ 0.11068980359934164,
+ 0.10958290556334822,
+ 0.10848707650771476,
+ 0.1074022057426376,
+ 0.10632818368521124,
+ 0.10526490184835911,
+ 0.10421225282987552,
+ 0.10317013030157676,
+ 0.102138428998561,
+ 0.10111704470857538,
+ 0.10010587426148965,
+ 0.09910481551887472,
+ 0.098113767363686,
+ 0.09713262969004911,
+ 0.09616130339314864,
+ 0.09519969035921716,
+ 0.09424769345562498,
+ 0.09330521652106873,
+ 0.09237216435585804,
+ 0.09144844271229946,
+ 0.09053395828517646,
+ 0.08962861870232469,
+ 0.08873233251530144,
+ 0.08784500919014843,
+ 0.08696655909824694,
+ 0.08609689350726446,
+ 0.08523592457219181,
+ 0.0843835653264699,
+ 0.0835397296732052,
+ 0.08270433237647315,
+ 0.08187728905270841,
+ 0.08105851616218133,
+ 0.08024793100055952,
+ 0.07944545169055392,
+ 0.07865099717364837,
+ 0.07786448720191189,
+ 0.07708584232989277,
+ 0.07631498390659384,
+ 0.07555183406752791,
+ 0.07479631572685264,
+ 0.07404835256958411,
+ 0.07330786904388827,
+ 0.07257479035344938,
+ 0.07184904244991488,
+ 0.07113055202541574,
+ 0.07041924650516158,
+ 0.06971505404010997,
+ 0.06901790349970886,
+ 0.06832772446471178,
+ 0.06764444722006466,
+ 0.066968002747864,
+ 0.06629832272038537,
+ 0.06563533949318151,
+ 0.06497898609824969,
+ 0.0643291962372672,
+ 0.06368590427489453,
+ 0.06304904523214558,
+ 0.06241855477982412,
+ 0.06179436923202588,
+ 0.06117642553970562,
+ 0.06056466128430856,
+ 0.05995901467146548,
+ 0.05935942452475082,
+ 0.058765830279503314,
+ 0.05817817197670828,
+ 0.057596390256941195,
+ 0.05702042635437178,
+ 0.05645022209082806,
+ 0.05588571986991978,
+ 0.055326862671220584,
+ 0.05477359404450838,
+ 0.054225858104063294,
+ 0.05368359952302266,
+ 0.053146763527792434,
+ 0.052615295892514506,
+ 0.05208914293358936,
+ 0.05156825150425347,
+ 0.051052568989210935,
+ 0.05054204329931883,
+ 0.05003662286632564,
+ 0.04953625663766238,
+ 0.04904089407128576,
+ 0.0485504851305729,
+ 0.048064980279267165,
+ 0.04758433047647449,
+ 0.04710848717170975,
+ 0.04663740229999265,
+ 0.04617102827699272,
+ 0.045709317994222794,
+ 0.04525222481428057,
+ 0.04479970256613776,
+ 0.04435170554047638,
+ 0.043908188485071616,
+ 0.0434691066002209,
+ 0.04303441553421869,
+ 0.0426040713788765,
+ 0.04217803066508773,
+ 0.04175625035843686,
+ 0.041338687854852486,
+ 0.04092530097630396,
+ 0.04051604796654091,
+ 0.04011088748687551,
+ 0.03970977861200675,
+ 0.03931268082588668,
+ 0.03891955401762781,
+ 0.03853035847745153,
+ 0.03814505489267701,
+ 0.03776360434375024,
+ 0.03738596830031274,
+ 0.03701210861730961,
+ 0.03664198753113651,
+ 0.036275567655825146,
+ 0.03591281197926689,
+ 0.035553683859474224,
+ 0.03519814702087948,
+ 0.03484616555067068,
+ 0.034497703895163975,
+ 0.03415272685621234,
+ 0.03381119958765021,
+ 0.03347308759177371,
+ 0.03313835671585597,
+ 0.03280697314869741,
+ 0.032478903417210436,
+ 0.032154114383038335,
+ 0.03183257323920795,
+ 0.03151424750681587,
+ 0.03119910503174771,
+ 0.030887113981430233,
+ 0.03057824284161593,
+ 0.03027246041319977,
+ 0.029969735809067772,
+ 0.029670038450977095,
+ 0.029373338066467324,
+ 0.02907960468580265,
+ 0.028788808638944625,
+ 0.028500920552555178,
+ 0.028215911347029624,
+ 0.027933752233559327,
+ 0.027654414711223735,
+ 0.027377870564111496,
+ 0.027104091858470385,
+ 0.026833050939885677,
+ 0.02656472043048682,
+ 0.02629907322618195,
+ 0.026036082493920133,
+ 0.02577572166898093,
+ 0.02551796445229112,
+ 0.02526278480776821,
+ 0.025010156959690527,
+ 0.02476005539009362,
+ 0.024512454836192684,
+ 0.024267330287830756,
+ 0.024024656984952448,
+ 0.023784410415102923,
+ 0.023546566310951898,
+ 0.023311100647842375,
+ 0.02307798964136395,
+ 0.022847209744950314,
+ 0.02261873764750081,
+ 0.022392550271025803,
+ 0.022168624768315544,
+ 0.02194693852063239,
+ 0.021727469135426065,
+ 0.021510194444071803,
+ 0.021295092499631085,
+ 0.021082141574634772,
+ 0.020871320158888425,
+ 0.020662606957299545,
+ 0.020455980887726547,
+ 0.02025142107884928,
+ 0.020048906868060788,
+ 0.01984841779938018,
+ 0.019649933621386374,
+ 0.019453434285172513,
+ 0.019258899942320787,
+ 0.01906631094289758,
+ 0.0188756478334686,
+ 0.018686891355133916,
+ 0.018500022441582577,
+ 0.01831502221716675,
+ 0.018131871994995084,
+ 0.017950553275045134,
+ 0.017771047742294682,
+ 0.017593337264871736,
+ 0.01741740389222302,
+ 0.01724322985330079,
+ 0.017070797554767782,
+ 0.016900089579220106,
+ 0.016731088683427906,
+ 0.016563777796593626,
+ 0.016398140018627688,
+ 0.01623415861844141,
+ 0.016071817032256998,
+ 0.01591109886193443,
+ 0.015751987873315085,
+ 0.015594467994581937,
+ 0.015438523314636117,
+ 0.015284138081489752,
+ 0.015131296700674856,
+ 0.014979983733668108,
+ 0.014830183896331426,
+ 0.014681882057368112,
+ 0.01453506323679443,
+ 0.014389712604426484,
+ 0.01424581547838222,
+ 0.014103357323598397,
+ 0.013962323750362412,
+ 0.013822700512858789,
+ 0.0136844735077302,
+ 0.0135476287726529,
+ 0.01341215248492637,
+ 0.013278030960077106,
+ 0.013145250650476337,
+ 0.01301379814397157,
+ 0.012883660162531854,
+ 0.012754823560906537,
+ 0.01262727532529747,
+ 0.012501002572044496,
+ 0.01237599254632405,
+ 0.01225223262086081,
+ 0.012129710294652202,
+ 0.01200841319170568,
+ 0.011888329059788623,
+ 0.011769445769190735,
+ 0.01165175131149883,
+ 0.011535233798383842,
+ 0.011419881460400004,
+ 0.011305682645796004,
+ 0.011192625819338045,
+ 0.011080699561144665,
+ 0.010969892565533218,
+ 0.010860193639877886,
+ 0.010751591703479106,
+ 0.010644075786444315,
+ 0.010537635028579871,
+ 0.010432258678294072,
+ 0.010327936091511131,
+ 0.010224656730596022,
+ 0.01012241016329006,
+ 0.01002118606165716,
+ 0.009920974201040588,
+ 0.009821764459030182,
+ 0.00972354681443988,
+ 0.00962631134629548,
+ 0.009530048232832523,
+ 0.0094347477505042,
+ 0.009340400272999157,
+ 0.009246996270269163,
+ 0.009154526307566474,
+ 0.009062981044490808,
+ 0.0089723512340459,
+ 0.00888262772170544,
+ 0.008793801444488386,
+ 0.008705863430043502,
+ 0.008618804795743068,
+ 0.008532616747785637,
+ 0.00844729058030778,
+ 0.008362817674504702,
+ 0.008279189497759654,
+ 0.008196397602782058,
+ 0.008114433626754238,
+ 0.008033289290486696,
+ 0.00795295639758183,
+ 0.00787342683360601,
+ 0.007794692565269949,
+ 0.00771674563961725,
+ 0.007639578183221077,
+ 0.007563182401388867,
+ 0.007487550577374978,
+ 0.007412675071601228,
+ 0.007338548320885215,
+ 0.0072651628376763635,
+ 0.0071925112092996,
+ 0.007120586097206604,
+ 0.007049380236234538,
+ 0.006978886433872193,
+ 0.006909097569533471,
+ 0.006840006593838137,
+ 0.0067716065278997555,
+ 0.006703890462620758,
+ 0.006636851557994551
+ ],
+ "yaxis": "y2"
+ },
+ {
+ "mode": "lines",
+ "name": "Score",
+ "type": "scatter",
+ "x": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 60,
+ 61,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 81,
+ 82,
+ 83,
+ 84,
+ 85,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 91,
+ 92,
+ 93,
+ 94,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 108,
+ 109,
+ 110,
+ 111,
+ 112,
+ 113,
+ 114,
+ 115,
+ 116,
+ 117,
+ 118,
+ 119,
+ 120,
+ 121,
+ 122,
+ 123,
+ 124,
+ 125,
+ 126,
+ 127,
+ 128,
+ 129,
+ 130,
+ 131,
+ 132,
+ 133,
+ 134,
+ 135,
+ 136,
+ 137,
+ 138,
+ 139,
+ 140,
+ 141,
+ 142,
+ 143,
+ 144,
+ 145,
+ 146,
+ 147,
+ 148,
+ 149,
+ 150,
+ 151,
+ 152,
+ 153,
+ 154,
+ 155,
+ 156,
+ 157,
+ 158,
+ 159,
+ 160,
+ 161,
+ 162,
+ 163,
+ 164,
+ 165,
+ 166,
+ 167,
+ 168,
+ 169,
+ 170,
+ 171,
+ 172,
+ 173,
+ 174,
+ 175,
+ 176,
+ 177,
+ 178,
+ 179,
+ 180,
+ 181,
+ 182,
+ 183,
+ 184,
+ 185,
+ 186,
+ 187,
+ 188,
+ 189,
+ 190,
+ 191,
+ 192,
+ 193,
+ 194,
+ 195,
+ 196,
+ 197,
+ 198,
+ 199,
+ 200,
+ 201,
+ 202,
+ 203,
+ 204,
+ 205,
+ 206,
+ 207,
+ 208,
+ 209,
+ 210,
+ 211,
+ 212,
+ 213,
+ 214,
+ 215,
+ 216,
+ 217,
+ 218,
+ 219,
+ 220,
+ 221,
+ 222,
+ 223,
+ 224,
+ 225,
+ 226,
+ 227,
+ 228,
+ 229,
+ 230,
+ 231,
+ 232,
+ 233,
+ 234,
+ 235,
+ 236,
+ 237,
+ 238,
+ 239,
+ 240,
+ 241,
+ 242,
+ 243,
+ 244,
+ 245,
+ 246,
+ 247,
+ 248,
+ 249,
+ 250,
+ 251,
+ 252,
+ 253,
+ 254,
+ 255,
+ 256,
+ 257,
+ 258,
+ 259,
+ 260,
+ 261,
+ 262,
+ 263,
+ 264,
+ 265,
+ 266,
+ 267,
+ 268,
+ 269,
+ 270,
+ 271,
+ 272,
+ 273,
+ 274,
+ 275,
+ 276,
+ 277,
+ 278,
+ 279,
+ 280,
+ 281,
+ 282,
+ 283,
+ 284,
+ 285,
+ 286,
+ 287,
+ 288,
+ 289,
+ 290,
+ 291,
+ 292,
+ 293,
+ 294,
+ 295,
+ 296,
+ 297,
+ 298,
+ 299,
+ 300,
+ 301,
+ 302,
+ 303,
+ 304,
+ 305,
+ 306,
+ 307,
+ 308,
+ 309,
+ 310,
+ 311,
+ 312,
+ 313,
+ 314,
+ 315,
+ 316,
+ 317,
+ 318,
+ 319,
+ 320,
+ 321,
+ 322,
+ 323,
+ 324,
+ 325,
+ 326,
+ 327,
+ 328,
+ 329,
+ 330,
+ 331,
+ 332,
+ 333,
+ 334,
+ 335,
+ 336,
+ 337,
+ 338,
+ 339,
+ 340,
+ 341,
+ 342,
+ 343,
+ 344,
+ 345,
+ 346,
+ 347,
+ 348,
+ 349,
+ 350,
+ 351,
+ 352,
+ 353,
+ 354,
+ 355,
+ 356,
+ 357,
+ 358,
+ 359,
+ 360,
+ 361,
+ 362,
+ 363,
+ 364,
+ 365,
+ 366,
+ 367,
+ 368,
+ 369,
+ 370,
+ 371,
+ 372,
+ 373,
+ 374,
+ 375,
+ 376,
+ 377,
+ 378,
+ 379,
+ 380,
+ 381,
+ 382,
+ 383,
+ 384,
+ 385,
+ 386,
+ 387,
+ 388,
+ 389,
+ 390,
+ 391,
+ 392,
+ 393,
+ 394,
+ 395,
+ 396,
+ 397,
+ 398,
+ 399,
+ 400,
+ 401,
+ 402,
+ 403,
+ 404,
+ 405,
+ 406,
+ 407,
+ 408,
+ 409,
+ 410,
+ 411,
+ 412,
+ 413,
+ 414,
+ 415,
+ 416,
+ 417,
+ 418,
+ 419,
+ 420,
+ 421,
+ 422,
+ 423,
+ 424,
+ 425,
+ 426,
+ 427,
+ 428,
+ 429,
+ 430,
+ 431,
+ 432,
+ 433,
+ 434,
+ 435,
+ 436,
+ 437,
+ 438,
+ 439,
+ 440,
+ 441,
+ 442,
+ 443,
+ 444,
+ 445,
+ 446,
+ 447,
+ 448,
+ 449,
+ 450,
+ 451,
+ 452,
+ 453,
+ 454,
+ 455,
+ 456,
+ 457,
+ 458,
+ 459,
+ 460,
+ 461,
+ 462,
+ 463,
+ 464,
+ 465,
+ 466,
+ 467,
+ 468,
+ 469,
+ 470,
+ 471,
+ 472,
+ 473,
+ 474,
+ 475,
+ 476,
+ 477,
+ 478,
+ 479,
+ 480,
+ 481,
+ 482,
+ 483,
+ 484,
+ 485,
+ 486,
+ 487,
+ 488,
+ 489,
+ 490,
+ 491,
+ 492,
+ 493,
+ 494,
+ 495,
+ 496,
+ 497,
+ 498,
+ 499
+ ],
+ "xaxis": "x",
+ "y": [
+ 13,
+ 18,
+ 19,
+ 20,
+ 23,
+ 14,
+ 18,
+ 13,
+ 46,
+ 18,
+ 20,
+ 14,
+ 15,
+ 12,
+ 35,
+ 30,
+ 21,
+ 36,
+ 18,
+ 10,
+ 17,
+ 11,
+ 20,
+ 65,
+ 17,
+ 13,
+ 14,
+ 68,
+ 30,
+ 90,
+ 20,
+ 17,
+ 41,
+ 14,
+ 10,
+ 15,
+ 23,
+ 20,
+ 23,
+ 100,
+ 19,
+ 39,
+ 53,
+ 21,
+ 15,
+ 19,
+ 47,
+ 92,
+ 32,
+ 28,
+ 21,
+ 74,
+ 89,
+ 22,
+ 16,
+ 24,
+ 62,
+ 81,
+ 48,
+ 61,
+ 47,
+ 82,
+ 47,
+ 100,
+ 33,
+ 70,
+ 100,
+ 96,
+ 62,
+ 44,
+ 39,
+ 34,
+ 55,
+ 100,
+ 36,
+ 100,
+ 100,
+ 59,
+ 48,
+ 39,
+ 100,
+ 49,
+ 67,
+ 30,
+ 96,
+ 76,
+ 50,
+ 100,
+ 95,
+ 38,
+ 47,
+ 42,
+ 100,
+ 32,
+ 69,
+ 90,
+ 63,
+ 97,
+ 65,
+ 56,
+ 95,
+ 58,
+ 51,
+ 40,
+ 97,
+ 91,
+ 48,
+ 100,
+ 100,
+ 81,
+ 100,
+ 81,
+ 47,
+ 33,
+ 100,
+ 48,
+ 60,
+ 45,
+ 100,
+ 10,
+ 97,
+ 95,
+ 100,
+ 100,
+ 21,
+ 100,
+ 40,
+ 100,
+ 91,
+ 57,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 40,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 63,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 90,
+ 91,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 64,
+ 97,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 90,
+ 100,
+ 92,
+ 100,
+ 100,
+ 63,
+ 100,
+ 100,
+ 100,
+ 87,
+ 17,
+ 100,
+ 100,
+ 100,
+ 68,
+ 100,
+ 100,
+ 98,
+ 100,
+ 100,
+ 61,
+ 100,
+ 39,
+ 58,
+ 100,
+ 100,
+ 100,
+ 58,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 84,
+ 100,
+ 60,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 97,
+ 66,
+ 82,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 67,
+ 100,
+ 100,
+ 28,
+ 100,
+ 100,
+ 100,
+ 100,
+ 97,
+ 51,
+ 97,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 40,
+ 100,
+ 100,
+ 100,
+ 100,
+ 90,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 54,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 74,
+ 100,
+ 54,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 71,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 98,
+ 100,
+ 100,
+ 100,
+ 40,
+ 100,
+ 100,
+ 45,
+ 100,
+ 54,
+ 100,
+ 83,
+ 100,
+ 43,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 55,
+ 100,
+ 100,
+ 100,
+ 100,
+ 50,
+ 100,
+ 100,
+ 96,
+ 100,
+ 100,
+ 100,
+ 45,
+ 100,
+ 45,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 90,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 58,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 69,
+ 100,
+ 63,
+ 100,
+ 80,
+ 71,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 46,
+ 55,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 65,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 62,
+ 100,
+ 100,
+ 95,
+ 100,
+ 100,
+ 80,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 40,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 61,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 89,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 58,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 80,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 61,
+ 100
+ ],
+ "yaxis": "y"
+ }
+ ],
+ "layout": {
+ "template": {
+ "data": {
+ "bar": [
+ {
+ "error_x": {
+ "color": "#2a3f5f"
+ },
+ "error_y": {
+ "color": "#2a3f5f"
+ },
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "bar"
+ }
+ ],
+ "barpolar": [
+ {
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "barpolar"
+ }
+ ],
+ "carpet": [
+ {
+ "aaxis": {
+ "endlinecolor": "#2a3f5f",
+ "gridcolor": "white",
+ "linecolor": "white",
+ "minorgridcolor": "white",
+ "startlinecolor": "#2a3f5f"
+ },
+ "baxis": {
+ "endlinecolor": "#2a3f5f",
+ "gridcolor": "white",
+ "linecolor": "white",
+ "minorgridcolor": "white",
+ "startlinecolor": "#2a3f5f"
+ },
+ "type": "carpet"
+ }
+ ],
+ "choropleth": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "choropleth"
+ }
+ ],
+ "contour": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "contour"
+ }
+ ],
+ "contourcarpet": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "contourcarpet"
+ }
+ ],
+ "heatmap": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "heatmap"
+ }
+ ],
+ "heatmapgl": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "heatmapgl"
+ }
+ ],
+ "histogram": [
+ {
+ "marker": {
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "histogram"
+ }
+ ],
+ "histogram2d": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "histogram2d"
+ }
+ ],
+ "histogram2dcontour": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "histogram2dcontour"
+ }
+ ],
+ "mesh3d": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "mesh3d"
+ }
+ ],
+ "parcoords": [
+ {
+ "line": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "parcoords"
+ }
+ ],
+ "pie": [
+ {
+ "automargin": true,
+ "type": "pie"
+ }
+ ],
+ "scatter": [
+ {
+ "fillpattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ },
+ "type": "scatter"
+ }
+ ],
+ "scatter3d": [
+ {
+ "line": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatter3d"
+ }
+ ],
+ "scattercarpet": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattercarpet"
+ }
+ ],
+ "scattergeo": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattergeo"
+ }
+ ],
+ "scattergl": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattergl"
+ }
+ ],
+ "scattermapbox": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattermapbox"
+ }
+ ],
+ "scatterpolar": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterpolar"
+ }
+ ],
+ "scatterpolargl": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterpolargl"
+ }
+ ],
+ "scatterternary": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterternary"
+ }
+ ],
+ "surface": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "surface"
+ }
+ ],
+ "table": [
+ {
+ "cells": {
+ "fill": {
+ "color": "#EBF0F8"
+ },
+ "line": {
+ "color": "white"
+ }
+ },
+ "header": {
+ "fill": {
+ "color": "#C8D4E3"
+ },
+ "line": {
+ "color": "white"
+ }
+ },
+ "type": "table"
+ }
+ ]
+ },
+ "layout": {
+ "annotationdefaults": {
+ "arrowcolor": "#2a3f5f",
+ "arrowhead": 0,
+ "arrowwidth": 1
+ },
+ "autotypenumbers": "strict",
+ "coloraxis": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "colorscale": {
+ "diverging": [
+ [
+ 0,
+ "#8e0152"
+ ],
+ [
+ 0.1,
+ "#c51b7d"
+ ],
+ [
+ 0.2,
+ "#de77ae"
+ ],
+ [
+ 0.3,
+ "#f1b6da"
+ ],
+ [
+ 0.4,
+ "#fde0ef"
+ ],
+ [
+ 0.5,
+ "#f7f7f7"
+ ],
+ [
+ 0.6,
+ "#e6f5d0"
+ ],
+ [
+ 0.7,
+ "#b8e186"
+ ],
+ [
+ 0.8,
+ "#7fbc41"
+ ],
+ [
+ 0.9,
+ "#4d9221"
+ ],
+ [
+ 1,
+ "#276419"
+ ]
+ ],
+ "sequential": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "sequentialminus": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ]
+ },
+ "colorway": [
+ "#636efa",
+ "#EF553B",
+ "#00cc96",
+ "#ab63fa",
+ "#FFA15A",
+ "#19d3f3",
+ "#FF6692",
+ "#B6E880",
+ "#FF97FF",
+ "#FECB52"
+ ],
+ "font": {
+ "color": "#2a3f5f"
+ },
+ "geo": {
+ "bgcolor": "white",
+ "lakecolor": "white",
+ "landcolor": "#E5ECF6",
+ "showlakes": true,
+ "showland": true,
+ "subunitcolor": "white"
+ },
+ "hoverlabel": {
+ "align": "left"
+ },
+ "hovermode": "closest",
+ "mapbox": {
+ "style": "light"
+ },
+ "paper_bgcolor": "white",
+ "plot_bgcolor": "#E5ECF6",
+ "polar": {
+ "angularaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "radialaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "scene": {
+ "xaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "yaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "zaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "caxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "DDQN Training, Advantage Type: AdvantageType.MAX"
+ },
+ "xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 0.94
+ ],
+ "title": {
+ "text": "Episode"
+ }
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Score"
+ }
+ },
+ "yaxis2": {
+ "anchor": "x",
+ "overlaying": "y",
+ "side": "right",
+ "title": {
+ "text": "Exploration Rate"
+ }
+ }
+ }
+ },
+ "image/png": "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",
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2992ae9493ab414f88e6e02d0076aea8",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/500 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "mode": "lines",
+ "name": "Exploration Rate",
+ "type": "scatter",
+ "x": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 60,
+ 61,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 81,
+ 82,
+ 83,
+ 84,
+ 85,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 91,
+ 92,
+ 93,
+ 94,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 108,
+ 109,
+ 110,
+ 111,
+ 112,
+ 113,
+ 114,
+ 115,
+ 116,
+ 117,
+ 118,
+ 119,
+ 120,
+ 121,
+ 122,
+ 123,
+ 124,
+ 125,
+ 126,
+ 127,
+ 128,
+ 129,
+ 130,
+ 131,
+ 132,
+ 133,
+ 134,
+ 135,
+ 136,
+ 137,
+ 138,
+ 139,
+ 140,
+ 141,
+ 142,
+ 143,
+ 144,
+ 145,
+ 146,
+ 147,
+ 148,
+ 149,
+ 150,
+ 151,
+ 152,
+ 153,
+ 154,
+ 155,
+ 156,
+ 157,
+ 158,
+ 159,
+ 160,
+ 161,
+ 162,
+ 163,
+ 164,
+ 165,
+ 166,
+ 167,
+ 168,
+ 169,
+ 170,
+ 171,
+ 172,
+ 173,
+ 174,
+ 175,
+ 176,
+ 177,
+ 178,
+ 179,
+ 180,
+ 181,
+ 182,
+ 183,
+ 184,
+ 185,
+ 186,
+ 187,
+ 188,
+ 189,
+ 190,
+ 191,
+ 192,
+ 193,
+ 194,
+ 195,
+ 196,
+ 197,
+ 198,
+ 199,
+ 200,
+ 201,
+ 202,
+ 203,
+ 204,
+ 205,
+ 206,
+ 207,
+ 208,
+ 209,
+ 210,
+ 211,
+ 212,
+ 213,
+ 214,
+ 215,
+ 216,
+ 217,
+ 218,
+ 219,
+ 220,
+ 221,
+ 222,
+ 223,
+ 224,
+ 225,
+ 226,
+ 227,
+ 228,
+ 229,
+ 230,
+ 231,
+ 232,
+ 233,
+ 234,
+ 235,
+ 236,
+ 237,
+ 238,
+ 239,
+ 240,
+ 241,
+ 242,
+ 243,
+ 244,
+ 245,
+ 246,
+ 247,
+ 248,
+ 249,
+ 250,
+ 251,
+ 252,
+ 253,
+ 254,
+ 255,
+ 256,
+ 257,
+ 258,
+ 259,
+ 260,
+ 261,
+ 262,
+ 263,
+ 264,
+ 265,
+ 266,
+ 267,
+ 268,
+ 269,
+ 270,
+ 271,
+ 272,
+ 273,
+ 274,
+ 275,
+ 276,
+ 277,
+ 278,
+ 279,
+ 280,
+ 281,
+ 282,
+ 283,
+ 284,
+ 285,
+ 286,
+ 287,
+ 288,
+ 289,
+ 290,
+ 291,
+ 292,
+ 293,
+ 294,
+ 295,
+ 296,
+ 297,
+ 298,
+ 299,
+ 300,
+ 301,
+ 302,
+ 303,
+ 304,
+ 305,
+ 306,
+ 307,
+ 308,
+ 309,
+ 310,
+ 311,
+ 312,
+ 313,
+ 314,
+ 315,
+ 316,
+ 317,
+ 318,
+ 319,
+ 320,
+ 321,
+ 322,
+ 323,
+ 324,
+ 325,
+ 326,
+ 327,
+ 328,
+ 329,
+ 330,
+ 331,
+ 332,
+ 333,
+ 334,
+ 335,
+ 336,
+ 337,
+ 338,
+ 339,
+ 340,
+ 341,
+ 342,
+ 343,
+ 344,
+ 345,
+ 346,
+ 347,
+ 348,
+ 349,
+ 350,
+ 351,
+ 352,
+ 353,
+ 354,
+ 355,
+ 356,
+ 357,
+ 358,
+ 359,
+ 360,
+ 361,
+ 362,
+ 363,
+ 364,
+ 365,
+ 366,
+ 367,
+ 368,
+ 369,
+ 370,
+ 371,
+ 372,
+ 373,
+ 374,
+ 375,
+ 376,
+ 377,
+ 378,
+ 379,
+ 380,
+ 381,
+ 382,
+ 383,
+ 384,
+ 385,
+ 386,
+ 387,
+ 388,
+ 389,
+ 390,
+ 391,
+ 392,
+ 393,
+ 394,
+ 395,
+ 396,
+ 397,
+ 398,
+ 399,
+ 400,
+ 401,
+ 402,
+ 403,
+ 404,
+ 405,
+ 406,
+ 407,
+ 408,
+ 409,
+ 410,
+ 411,
+ 412,
+ 413,
+ 414,
+ 415,
+ 416,
+ 417,
+ 418,
+ 419,
+ 420,
+ 421,
+ 422,
+ 423,
+ 424,
+ 425,
+ 426,
+ 427,
+ 428,
+ 429,
+ 430,
+ 431,
+ 432,
+ 433,
+ 434,
+ 435,
+ 436,
+ 437,
+ 438,
+ 439,
+ 440,
+ 441,
+ 442,
+ 443,
+ 444,
+ 445,
+ 446,
+ 447,
+ 448,
+ 449,
+ 450,
+ 451,
+ 452,
+ 453,
+ 454,
+ 455,
+ 456,
+ 457,
+ 458,
+ 459,
+ 460,
+ 461,
+ 462,
+ 463,
+ 464,
+ 465,
+ 466,
+ 467,
+ 468,
+ 469,
+ 470,
+ 471,
+ 472,
+ 473,
+ 474,
+ 475,
+ 476,
+ 477,
+ 478,
+ 479,
+ 480,
+ 481,
+ 482,
+ 483,
+ 484,
+ 485,
+ 486,
+ 487,
+ 488,
+ 489,
+ 490,
+ 491,
+ 492,
+ 493,
+ 494,
+ 495,
+ 496,
+ 497,
+ 498,
+ 499
+ ],
+ "xaxis": "x",
+ "y": [
+ 1,
+ 0.99,
+ 0.9801,
+ 0.970299,
+ 0.96059601,
+ 0.9509900499,
+ 0.941480149401,
+ 0.93206534790699,
+ 0.92274469442792,
+ 0.9135172474836408,
+ 0.9043820750088044,
+ 0.8953382542587163,
+ 0.8863848717161291,
+ 0.8775210229989678,
+ 0.8687458127689781,
+ 0.8600583546412883,
+ 0.8514577710948754,
+ 0.8429431933839266,
+ 0.8345137614500874,
+ 0.8261686238355865,
+ 0.8179069375972307,
+ 0.8097278682212583,
+ 0.8016305895390458,
+ 0.7936142836436553,
+ 0.7856781408072188,
+ 0.7778213593991465,
+ 0.7700431458051551,
+ 0.7623427143471035,
+ 0.7547192872036325,
+ 0.7471720943315961,
+ 0.7397003733882802,
+ 0.7323033696543974,
+ 0.7249803359578534,
+ 0.7177305325982748,
+ 0.7105532272722921,
+ 0.7034476949995692,
+ 0.6964132180495735,
+ 0.6894490858690777,
+ 0.682554595010387,
+ 0.6757290490602831,
+ 0.6689717585696803,
+ 0.6622820409839835,
+ 0.6556592205741436,
+ 0.6491026283684022,
+ 0.6426116020847181,
+ 0.6361854860638709,
+ 0.6298236312032323,
+ 0.6235253948912,
+ 0.617290140942288,
+ 0.6111172395328651,
+ 0.6050060671375365,
+ 0.5989560064661611,
+ 0.5929664464014994,
+ 0.5870367819374844,
+ 0.5811664141181095,
+ 0.5753547499769285,
+ 0.5696012024771592,
+ 0.5639051904523876,
+ 0.5582661385478638,
+ 0.5526834771623851,
+ 0.5471566423907612,
+ 0.5416850759668536,
+ 0.536268225207185,
+ 0.5309055429551132,
+ 0.525596487525562,
+ 0.5203405226503064,
+ 0.5151371174238033,
+ 0.5099857462495653,
+ 0.5048858887870696,
+ 0.4998370298991989,
+ 0.49483865960020695,
+ 0.4898902730042049,
+ 0.4849913702741629,
+ 0.4801414565714212,
+ 0.475340042005707,
+ 0.47058664158564995,
+ 0.4658807751697934,
+ 0.4612219674180955,
+ 0.45660974774391455,
+ 0.4520436502664754,
+ 0.44752321376381066,
+ 0.44304798162617254,
+ 0.4386175018099108,
+ 0.4342313267918117,
+ 0.4298890135238936,
+ 0.42559012338865465,
+ 0.4213342221547681,
+ 0.4171208799332205,
+ 0.41294967113388825,
+ 0.40882017442254937,
+ 0.4047319726783239,
+ 0.40068465295154065,
+ 0.39667780642202527,
+ 0.392711028357805,
+ 0.38878391807422696,
+ 0.3848960788934847,
+ 0.38104711810454983,
+ 0.37723664692350434,
+ 0.37346428045426927,
+ 0.36972963764972655,
+ 0.36603234127322926,
+ 0.36237201786049694,
+ 0.358748297681892,
+ 0.35516081470507305,
+ 0.3516092065580223,
+ 0.34809311449244207,
+ 0.34461218334751764,
+ 0.34116606151404244,
+ 0.337754400898902,
+ 0.334376856889913,
+ 0.33103308832101386,
+ 0.3277227574378037,
+ 0.3244455298634257,
+ 0.3212010745647914,
+ 0.3179890638191435,
+ 0.31480917318095203,
+ 0.3116610814491425,
+ 0.30854447063465107,
+ 0.30545902592830454,
+ 0.3024044356690215,
+ 0.29938039131233124,
+ 0.2963865873992079,
+ 0.29342272152521587,
+ 0.2904884943099637,
+ 0.28758360936686406,
+ 0.2847077732731954,
+ 0.28186069554046345,
+ 0.2790420885850588,
+ 0.2762516676992082,
+ 0.27348915102221616,
+ 0.270754259511994,
+ 0.26804671691687404,
+ 0.2653662497477053,
+ 0.2627125872502282,
+ 0.2600854613777259,
+ 0.2574846067639487,
+ 0.2549097606963092,
+ 0.2523606630893461,
+ 0.24983705645845267,
+ 0.24733868589386815,
+ 0.24486529903492943,
+ 0.24241664604458016,
+ 0.23999247958413436,
+ 0.23759255478829305,
+ 0.2352166292404101,
+ 0.232864462948006,
+ 0.23053581831852593,
+ 0.22823046013534068,
+ 0.22594815553398728,
+ 0.22368867397864745,
+ 0.22145178723886097,
+ 0.2192372693664723,
+ 0.2170448966728076,
+ 0.21487444770607952,
+ 0.2127257032290187,
+ 0.21059844619672857,
+ 0.20849246173476127,
+ 0.2064075371174137,
+ 0.20434346174623952,
+ 0.20230002712877712,
+ 0.20027702685748935,
+ 0.19827425658891443,
+ 0.1962915140230253,
+ 0.19432859888279505,
+ 0.1923853128939671,
+ 0.19046145976502743,
+ 0.1885568451673771,
+ 0.18667127671570335,
+ 0.18480456394854633,
+ 0.18295651830906087,
+ 0.18112695312597027,
+ 0.17931568359471056,
+ 0.17752252675876343,
+ 0.17574730149117582,
+ 0.17398982847626407,
+ 0.17224993019150142,
+ 0.1705274308895864,
+ 0.16882215658069055,
+ 0.16713393501488363,
+ 0.1654625956647348,
+ 0.16380796970808745,
+ 0.16216989001100657,
+ 0.1605481911108965,
+ 0.15894270919978754,
+ 0.15735328210778965,
+ 0.15577974928671176,
+ 0.15422195179384465,
+ 0.1526797322759062,
+ 0.15115293495314713,
+ 0.14964140560361566,
+ 0.1481449915475795,
+ 0.1466635416321037,
+ 0.14519690621578268,
+ 0.14374493715362485,
+ 0.1423074877820886,
+ 0.1408844129042677,
+ 0.13947556877522502,
+ 0.13808081308747278,
+ 0.13670000495659804,
+ 0.13533300490703207,
+ 0.13397967485796175,
+ 0.13263987810938213,
+ 0.1313134793282883,
+ 0.13000034453500542,
+ 0.12870034108965536,
+ 0.1274133376787588,
+ 0.12613920430197123,
+ 0.12487781225895152,
+ 0.123629034136362,
+ 0.12239274379499838,
+ 0.1211688163570484,
+ 0.11995712819347792,
+ 0.11875755691154316,
+ 0.11756998134242772,
+ 0.11639428152900344,
+ 0.1152303387137134,
+ 0.11407803532657627,
+ 0.11293725497331052,
+ 0.1118078824235774,
+ 0.11068980359934164,
+ 0.10958290556334822,
+ 0.10848707650771476,
+ 0.1074022057426376,
+ 0.10632818368521124,
+ 0.10526490184835911,
+ 0.10421225282987552,
+ 0.10317013030157676,
+ 0.102138428998561,
+ 0.10111704470857538,
+ 0.10010587426148965,
+ 0.09910481551887472,
+ 0.098113767363686,
+ 0.09713262969004911,
+ 0.09616130339314864,
+ 0.09519969035921716,
+ 0.09424769345562498,
+ 0.09330521652106873,
+ 0.09237216435585804,
+ 0.09144844271229946,
+ 0.09053395828517646,
+ 0.08962861870232469,
+ 0.08873233251530144,
+ 0.08784500919014843,
+ 0.08696655909824694,
+ 0.08609689350726446,
+ 0.08523592457219181,
+ 0.0843835653264699,
+ 0.0835397296732052,
+ 0.08270433237647315,
+ 0.08187728905270841,
+ 0.08105851616218133,
+ 0.08024793100055952,
+ 0.07944545169055392,
+ 0.07865099717364837,
+ 0.07786448720191189,
+ 0.07708584232989277,
+ 0.07631498390659384,
+ 0.07555183406752791,
+ 0.07479631572685264,
+ 0.07404835256958411,
+ 0.07330786904388827,
+ 0.07257479035344938,
+ 0.07184904244991488,
+ 0.07113055202541574,
+ 0.07041924650516158,
+ 0.06971505404010997,
+ 0.06901790349970886,
+ 0.06832772446471178,
+ 0.06764444722006466,
+ 0.066968002747864,
+ 0.06629832272038537,
+ 0.06563533949318151,
+ 0.06497898609824969,
+ 0.0643291962372672,
+ 0.06368590427489453,
+ 0.06304904523214558,
+ 0.06241855477982412,
+ 0.06179436923202588,
+ 0.06117642553970562,
+ 0.06056466128430856,
+ 0.05995901467146548,
+ 0.05935942452475082,
+ 0.058765830279503314,
+ 0.05817817197670828,
+ 0.057596390256941195,
+ 0.05702042635437178,
+ 0.05645022209082806,
+ 0.05588571986991978,
+ 0.055326862671220584,
+ 0.05477359404450838,
+ 0.054225858104063294,
+ 0.05368359952302266,
+ 0.053146763527792434,
+ 0.052615295892514506,
+ 0.05208914293358936,
+ 0.05156825150425347,
+ 0.051052568989210935,
+ 0.05054204329931883,
+ 0.05003662286632564,
+ 0.04953625663766238,
+ 0.04904089407128576,
+ 0.0485504851305729,
+ 0.048064980279267165,
+ 0.04758433047647449,
+ 0.04710848717170975,
+ 0.04663740229999265,
+ 0.04617102827699272,
+ 0.045709317994222794,
+ 0.04525222481428057,
+ 0.04479970256613776,
+ 0.04435170554047638,
+ 0.043908188485071616,
+ 0.0434691066002209,
+ 0.04303441553421869,
+ 0.0426040713788765,
+ 0.04217803066508773,
+ 0.04175625035843686,
+ 0.041338687854852486,
+ 0.04092530097630396,
+ 0.04051604796654091,
+ 0.04011088748687551,
+ 0.03970977861200675,
+ 0.03931268082588668,
+ 0.03891955401762781,
+ 0.03853035847745153,
+ 0.03814505489267701,
+ 0.03776360434375024,
+ 0.03738596830031274,
+ 0.03701210861730961,
+ 0.03664198753113651,
+ 0.036275567655825146,
+ 0.03591281197926689,
+ 0.035553683859474224,
+ 0.03519814702087948,
+ 0.03484616555067068,
+ 0.034497703895163975,
+ 0.03415272685621234,
+ 0.03381119958765021,
+ 0.03347308759177371,
+ 0.03313835671585597,
+ 0.03280697314869741,
+ 0.032478903417210436,
+ 0.032154114383038335,
+ 0.03183257323920795,
+ 0.03151424750681587,
+ 0.03119910503174771,
+ 0.030887113981430233,
+ 0.03057824284161593,
+ 0.03027246041319977,
+ 0.029969735809067772,
+ 0.029670038450977095,
+ 0.029373338066467324,
+ 0.02907960468580265,
+ 0.028788808638944625,
+ 0.028500920552555178,
+ 0.028215911347029624,
+ 0.027933752233559327,
+ 0.027654414711223735,
+ 0.027377870564111496,
+ 0.027104091858470385,
+ 0.026833050939885677,
+ 0.02656472043048682,
+ 0.02629907322618195,
+ 0.026036082493920133,
+ 0.02577572166898093,
+ 0.02551796445229112,
+ 0.02526278480776821,
+ 0.025010156959690527,
+ 0.02476005539009362,
+ 0.024512454836192684,
+ 0.024267330287830756,
+ 0.024024656984952448,
+ 0.023784410415102923,
+ 0.023546566310951898,
+ 0.023311100647842375,
+ 0.02307798964136395,
+ 0.022847209744950314,
+ 0.02261873764750081,
+ 0.022392550271025803,
+ 0.022168624768315544,
+ 0.02194693852063239,
+ 0.021727469135426065,
+ 0.021510194444071803,
+ 0.021295092499631085,
+ 0.021082141574634772,
+ 0.020871320158888425,
+ 0.020662606957299545,
+ 0.020455980887726547,
+ 0.02025142107884928,
+ 0.020048906868060788,
+ 0.01984841779938018,
+ 0.019649933621386374,
+ 0.019453434285172513,
+ 0.019258899942320787,
+ 0.01906631094289758,
+ 0.0188756478334686,
+ 0.018686891355133916,
+ 0.018500022441582577,
+ 0.01831502221716675,
+ 0.018131871994995084,
+ 0.017950553275045134,
+ 0.017771047742294682,
+ 0.017593337264871736,
+ 0.01741740389222302,
+ 0.01724322985330079,
+ 0.017070797554767782,
+ 0.016900089579220106,
+ 0.016731088683427906,
+ 0.016563777796593626,
+ 0.016398140018627688,
+ 0.01623415861844141,
+ 0.016071817032256998,
+ 0.01591109886193443,
+ 0.015751987873315085,
+ 0.015594467994581937,
+ 0.015438523314636117,
+ 0.015284138081489752,
+ 0.015131296700674856,
+ 0.014979983733668108,
+ 0.014830183896331426,
+ 0.014681882057368112,
+ 0.01453506323679443,
+ 0.014389712604426484,
+ 0.01424581547838222,
+ 0.014103357323598397,
+ 0.013962323750362412,
+ 0.013822700512858789,
+ 0.0136844735077302,
+ 0.0135476287726529,
+ 0.01341215248492637,
+ 0.013278030960077106,
+ 0.013145250650476337,
+ 0.01301379814397157,
+ 0.012883660162531854,
+ 0.012754823560906537,
+ 0.01262727532529747,
+ 0.012501002572044496,
+ 0.01237599254632405,
+ 0.01225223262086081,
+ 0.012129710294652202,
+ 0.01200841319170568,
+ 0.011888329059788623,
+ 0.011769445769190735,
+ 0.01165175131149883,
+ 0.011535233798383842,
+ 0.011419881460400004,
+ 0.011305682645796004,
+ 0.011192625819338045,
+ 0.011080699561144665,
+ 0.010969892565533218,
+ 0.010860193639877886,
+ 0.010751591703479106,
+ 0.010644075786444315,
+ 0.010537635028579871,
+ 0.010432258678294072,
+ 0.010327936091511131,
+ 0.010224656730596022,
+ 0.01012241016329006,
+ 0.01002118606165716,
+ 0.009920974201040588,
+ 0.009821764459030182,
+ 0.00972354681443988,
+ 0.00962631134629548,
+ 0.009530048232832523,
+ 0.0094347477505042,
+ 0.009340400272999157,
+ 0.009246996270269163,
+ 0.009154526307566474,
+ 0.009062981044490808,
+ 0.0089723512340459,
+ 0.00888262772170544,
+ 0.008793801444488386,
+ 0.008705863430043502,
+ 0.008618804795743068,
+ 0.008532616747785637,
+ 0.00844729058030778,
+ 0.008362817674504702,
+ 0.008279189497759654,
+ 0.008196397602782058,
+ 0.008114433626754238,
+ 0.008033289290486696,
+ 0.00795295639758183,
+ 0.00787342683360601,
+ 0.007794692565269949,
+ 0.00771674563961725,
+ 0.007639578183221077,
+ 0.007563182401388867,
+ 0.007487550577374978,
+ 0.007412675071601228,
+ 0.007338548320885215,
+ 0.0072651628376763635,
+ 0.0071925112092996,
+ 0.007120586097206604,
+ 0.007049380236234538,
+ 0.006978886433872193,
+ 0.006909097569533471,
+ 0.006840006593838137,
+ 0.0067716065278997555,
+ 0.006703890462620758,
+ 0.006636851557994551
+ ],
+ "yaxis": "y2"
+ },
+ {
+ "mode": "lines",
+ "name": "Score",
+ "type": "scatter",
+ "x": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 60,
+ 61,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 81,
+ 82,
+ 83,
+ 84,
+ 85,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 91,
+ 92,
+ 93,
+ 94,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 108,
+ 109,
+ 110,
+ 111,
+ 112,
+ 113,
+ 114,
+ 115,
+ 116,
+ 117,
+ 118,
+ 119,
+ 120,
+ 121,
+ 122,
+ 123,
+ 124,
+ 125,
+ 126,
+ 127,
+ 128,
+ 129,
+ 130,
+ 131,
+ 132,
+ 133,
+ 134,
+ 135,
+ 136,
+ 137,
+ 138,
+ 139,
+ 140,
+ 141,
+ 142,
+ 143,
+ 144,
+ 145,
+ 146,
+ 147,
+ 148,
+ 149,
+ 150,
+ 151,
+ 152,
+ 153,
+ 154,
+ 155,
+ 156,
+ 157,
+ 158,
+ 159,
+ 160,
+ 161,
+ 162,
+ 163,
+ 164,
+ 165,
+ 166,
+ 167,
+ 168,
+ 169,
+ 170,
+ 171,
+ 172,
+ 173,
+ 174,
+ 175,
+ 176,
+ 177,
+ 178,
+ 179,
+ 180,
+ 181,
+ 182,
+ 183,
+ 184,
+ 185,
+ 186,
+ 187,
+ 188,
+ 189,
+ 190,
+ 191,
+ 192,
+ 193,
+ 194,
+ 195,
+ 196,
+ 197,
+ 198,
+ 199,
+ 200,
+ 201,
+ 202,
+ 203,
+ 204,
+ 205,
+ 206,
+ 207,
+ 208,
+ 209,
+ 210,
+ 211,
+ 212,
+ 213,
+ 214,
+ 215,
+ 216,
+ 217,
+ 218,
+ 219,
+ 220,
+ 221,
+ 222,
+ 223,
+ 224,
+ 225,
+ 226,
+ 227,
+ 228,
+ 229,
+ 230,
+ 231,
+ 232,
+ 233,
+ 234,
+ 235,
+ 236,
+ 237,
+ 238,
+ 239,
+ 240,
+ 241,
+ 242,
+ 243,
+ 244,
+ 245,
+ 246,
+ 247,
+ 248,
+ 249,
+ 250,
+ 251,
+ 252,
+ 253,
+ 254,
+ 255,
+ 256,
+ 257,
+ 258,
+ 259,
+ 260,
+ 261,
+ 262,
+ 263,
+ 264,
+ 265,
+ 266,
+ 267,
+ 268,
+ 269,
+ 270,
+ 271,
+ 272,
+ 273,
+ 274,
+ 275,
+ 276,
+ 277,
+ 278,
+ 279,
+ 280,
+ 281,
+ 282,
+ 283,
+ 284,
+ 285,
+ 286,
+ 287,
+ 288,
+ 289,
+ 290,
+ 291,
+ 292,
+ 293,
+ 294,
+ 295,
+ 296,
+ 297,
+ 298,
+ 299,
+ 300,
+ 301,
+ 302,
+ 303,
+ 304,
+ 305,
+ 306,
+ 307,
+ 308,
+ 309,
+ 310,
+ 311,
+ 312,
+ 313,
+ 314,
+ 315,
+ 316,
+ 317,
+ 318,
+ 319,
+ 320,
+ 321,
+ 322,
+ 323,
+ 324,
+ 325,
+ 326,
+ 327,
+ 328,
+ 329,
+ 330,
+ 331,
+ 332,
+ 333,
+ 334,
+ 335,
+ 336,
+ 337,
+ 338,
+ 339,
+ 340,
+ 341,
+ 342,
+ 343,
+ 344,
+ 345,
+ 346,
+ 347,
+ 348,
+ 349,
+ 350,
+ 351,
+ 352,
+ 353,
+ 354,
+ 355,
+ 356,
+ 357,
+ 358,
+ 359,
+ 360,
+ 361,
+ 362,
+ 363,
+ 364,
+ 365,
+ 366,
+ 367,
+ 368,
+ 369,
+ 370,
+ 371,
+ 372,
+ 373,
+ 374,
+ 375,
+ 376,
+ 377,
+ 378,
+ 379,
+ 380,
+ 381,
+ 382,
+ 383,
+ 384,
+ 385,
+ 386,
+ 387,
+ 388,
+ 389,
+ 390,
+ 391,
+ 392,
+ 393,
+ 394,
+ 395,
+ 396,
+ 397,
+ 398,
+ 399,
+ 400,
+ 401,
+ 402,
+ 403,
+ 404,
+ 405,
+ 406,
+ 407,
+ 408,
+ 409,
+ 410,
+ 411,
+ 412,
+ 413,
+ 414,
+ 415,
+ 416,
+ 417,
+ 418,
+ 419,
+ 420,
+ 421,
+ 422,
+ 423,
+ 424,
+ 425,
+ 426,
+ 427,
+ 428,
+ 429,
+ 430,
+ 431,
+ 432,
+ 433,
+ 434,
+ 435,
+ 436,
+ 437,
+ 438,
+ 439,
+ 440,
+ 441,
+ 442,
+ 443,
+ 444,
+ 445,
+ 446,
+ 447,
+ 448,
+ 449,
+ 450,
+ 451,
+ 452,
+ 453,
+ 454,
+ 455,
+ 456,
+ 457,
+ 458,
+ 459,
+ 460,
+ 461,
+ 462,
+ 463,
+ 464,
+ 465,
+ 466,
+ 467,
+ 468,
+ 469,
+ 470,
+ 471,
+ 472,
+ 473,
+ 474,
+ 475,
+ 476,
+ 477,
+ 478,
+ 479,
+ 480,
+ 481,
+ 482,
+ 483,
+ 484,
+ 485,
+ 486,
+ 487,
+ 488,
+ 489,
+ 490,
+ 491,
+ 492,
+ 493,
+ 494,
+ 495,
+ 496,
+ 497,
+ 498,
+ 499
+ ],
+ "xaxis": "x",
+ "y": [
+ 14,
+ 15,
+ 16,
+ 18,
+ 19,
+ 33,
+ 10,
+ 26,
+ 28,
+ 52,
+ 17,
+ 19,
+ 48,
+ 10,
+ 19,
+ 20,
+ 17,
+ 18,
+ 16,
+ 36,
+ 15,
+ 56,
+ 23,
+ 12,
+ 19,
+ 81,
+ 34,
+ 9,
+ 28,
+ 31,
+ 35,
+ 45,
+ 21,
+ 10,
+ 11,
+ 49,
+ 13,
+ 34,
+ 34,
+ 60,
+ 17,
+ 15,
+ 67,
+ 17,
+ 23,
+ 24,
+ 21,
+ 13,
+ 16,
+ 40,
+ 33,
+ 13,
+ 56,
+ 35,
+ 51,
+ 31,
+ 42,
+ 100,
+ 28,
+ 58,
+ 59,
+ 20,
+ 23,
+ 71,
+ 46,
+ 13,
+ 82,
+ 52,
+ 35,
+ 43,
+ 23,
+ 47,
+ 66,
+ 52,
+ 100,
+ 60,
+ 99,
+ 25,
+ 32,
+ 78,
+ 72,
+ 43,
+ 67,
+ 39,
+ 100,
+ 100,
+ 63,
+ 37,
+ 91,
+ 34,
+ 40,
+ 37,
+ 100,
+ 42,
+ 46,
+ 20,
+ 80,
+ 36,
+ 100,
+ 100,
+ 100,
+ 45,
+ 100,
+ 100,
+ 100,
+ 84,
+ 39,
+ 90,
+ 52,
+ 38,
+ 100,
+ 70,
+ 100,
+ 100,
+ 100,
+ 30,
+ 49,
+ 100,
+ 18,
+ 40,
+ 41,
+ 43,
+ 100,
+ 89,
+ 100,
+ 66,
+ 55,
+ 72,
+ 31,
+ 50,
+ 100,
+ 49,
+ 57,
+ 100,
+ 100,
+ 71,
+ 30,
+ 73,
+ 72,
+ 46,
+ 51,
+ 37,
+ 53,
+ 96,
+ 49,
+ 70,
+ 61,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 68,
+ 53,
+ 80,
+ 54,
+ 100,
+ 100,
+ 40,
+ 100,
+ 100,
+ 100,
+ 100,
+ 72,
+ 63,
+ 68,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 29,
+ 57,
+ 74,
+ 47,
+ 89,
+ 91,
+ 100,
+ 100,
+ 100,
+ 30,
+ 100,
+ 88,
+ 100,
+ 100,
+ 100,
+ 91,
+ 100,
+ 100,
+ 23,
+ 90,
+ 100,
+ 100,
+ 100,
+ 61,
+ 100,
+ 100,
+ 100,
+ 75,
+ 83,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 69,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 81,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 63,
+ 31,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 84,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 57,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 90,
+ 100,
+ 100,
+ 87,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 53,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 59,
+ 100,
+ 100,
+ 100,
+ 71,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 97,
+ 100,
+ 100,
+ 100,
+ 100,
+ 23,
+ 100,
+ 92,
+ 100,
+ 100,
+ 100,
+ 89,
+ 100,
+ 61,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 96,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 70,
+ 100,
+ 100,
+ 100,
+ 92,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 45,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 72,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 53,
+ 100,
+ 78,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 84,
+ 100,
+ 86,
+ 86,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 50,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 38,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100
+ ],
+ "yaxis": "y"
+ }
+ ],
+ "layout": {
+ "template": {
+ "data": {
+ "bar": [
+ {
+ "error_x": {
+ "color": "#2a3f5f"
+ },
+ "error_y": {
+ "color": "#2a3f5f"
+ },
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "bar"
+ }
+ ],
+ "barpolar": [
+ {
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "barpolar"
+ }
+ ],
+ "carpet": [
+ {
+ "aaxis": {
+ "endlinecolor": "#2a3f5f",
+ "gridcolor": "white",
+ "linecolor": "white",
+ "minorgridcolor": "white",
+ "startlinecolor": "#2a3f5f"
+ },
+ "baxis": {
+ "endlinecolor": "#2a3f5f",
+ "gridcolor": "white",
+ "linecolor": "white",
+ "minorgridcolor": "white",
+ "startlinecolor": "#2a3f5f"
+ },
+ "type": "carpet"
+ }
+ ],
+ "choropleth": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "choropleth"
+ }
+ ],
+ "contour": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "contour"
+ }
+ ],
+ "contourcarpet": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "contourcarpet"
+ }
+ ],
+ "heatmap": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "heatmap"
+ }
+ ],
+ "heatmapgl": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "heatmapgl"
+ }
+ ],
+ "histogram": [
+ {
+ "marker": {
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "histogram"
+ }
+ ],
+ "histogram2d": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "histogram2d"
+ }
+ ],
+ "histogram2dcontour": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "histogram2dcontour"
+ }
+ ],
+ "mesh3d": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "mesh3d"
+ }
+ ],
+ "parcoords": [
+ {
+ "line": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "parcoords"
+ }
+ ],
+ "pie": [
+ {
+ "automargin": true,
+ "type": "pie"
+ }
+ ],
+ "scatter": [
+ {
+ "fillpattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ },
+ "type": "scatter"
+ }
+ ],
+ "scatter3d": [
+ {
+ "line": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatter3d"
+ }
+ ],
+ "scattercarpet": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattercarpet"
+ }
+ ],
+ "scattergeo": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattergeo"
+ }
+ ],
+ "scattergl": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattergl"
+ }
+ ],
+ "scattermapbox": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattermapbox"
+ }
+ ],
+ "scatterpolar": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterpolar"
+ }
+ ],
+ "scatterpolargl": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterpolargl"
+ }
+ ],
+ "scatterternary": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterternary"
+ }
+ ],
+ "surface": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "surface"
+ }
+ ],
+ "table": [
+ {
+ "cells": {
+ "fill": {
+ "color": "#EBF0F8"
+ },
+ "line": {
+ "color": "white"
+ }
+ },
+ "header": {
+ "fill": {
+ "color": "#C8D4E3"
+ },
+ "line": {
+ "color": "white"
+ }
+ },
+ "type": "table"
+ }
+ ]
+ },
+ "layout": {
+ "annotationdefaults": {
+ "arrowcolor": "#2a3f5f",
+ "arrowhead": 0,
+ "arrowwidth": 1
+ },
+ "autotypenumbers": "strict",
+ "coloraxis": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "colorscale": {
+ "diverging": [
+ [
+ 0,
+ "#8e0152"
+ ],
+ [
+ 0.1,
+ "#c51b7d"
+ ],
+ [
+ 0.2,
+ "#de77ae"
+ ],
+ [
+ 0.3,
+ "#f1b6da"
+ ],
+ [
+ 0.4,
+ "#fde0ef"
+ ],
+ [
+ 0.5,
+ "#f7f7f7"
+ ],
+ [
+ 0.6,
+ "#e6f5d0"
+ ],
+ [
+ 0.7,
+ "#b8e186"
+ ],
+ [
+ 0.8,
+ "#7fbc41"
+ ],
+ [
+ 0.9,
+ "#4d9221"
+ ],
+ [
+ 1,
+ "#276419"
+ ]
+ ],
+ "sequential": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "sequentialminus": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ]
+ },
+ "colorway": [
+ "#636efa",
+ "#EF553B",
+ "#00cc96",
+ "#ab63fa",
+ "#FFA15A",
+ "#19d3f3",
+ "#FF6692",
+ "#B6E880",
+ "#FF97FF",
+ "#FECB52"
+ ],
+ "font": {
+ "color": "#2a3f5f"
+ },
+ "geo": {
+ "bgcolor": "white",
+ "lakecolor": "white",
+ "landcolor": "#E5ECF6",
+ "showlakes": true,
+ "showland": true,
+ "subunitcolor": "white"
+ },
+ "hoverlabel": {
+ "align": "left"
+ },
+ "hovermode": "closest",
+ "mapbox": {
+ "style": "light"
+ },
+ "paper_bgcolor": "white",
+ "plot_bgcolor": "#E5ECF6",
+ "polar": {
+ "angularaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "radialaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "scene": {
+ "xaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "yaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "zaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "caxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "DDQN Training, Advantage Type: AdvantageType.AVG"
+ },
+ "xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 0.94
+ ],
+ "title": {
+ "text": "Episode"
+ }
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Score"
+ }
+ },
+ "yaxis2": {
+ "anchor": "x",
+ "overlaying": "y",
+ "side": "right",
+ "title": {
+ "text": "Exploration Rate"
+ }
+ }
+ }
+ },
+ "image/png": "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",
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a5eae2c06aad46cfa1ba9bc887fdf73a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/500 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "mode": "lines",
+ "name": "Exploration Rate",
+ "type": "scatter",
+ "x": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 60,
+ 61,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 81,
+ 82,
+ 83,
+ 84,
+ 85,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 91,
+ 92,
+ 93,
+ 94,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 108,
+ 109,
+ 110,
+ 111,
+ 112,
+ 113,
+ 114,
+ 115,
+ 116,
+ 117,
+ 118,
+ 119,
+ 120,
+ 121,
+ 122,
+ 123,
+ 124,
+ 125,
+ 126,
+ 127,
+ 128,
+ 129,
+ 130,
+ 131,
+ 132,
+ 133,
+ 134,
+ 135,
+ 136,
+ 137,
+ 138,
+ 139,
+ 140,
+ 141,
+ 142,
+ 143,
+ 144,
+ 145,
+ 146,
+ 147,
+ 148,
+ 149,
+ 150,
+ 151,
+ 152,
+ 153,
+ 154,
+ 155,
+ 156,
+ 157,
+ 158,
+ 159,
+ 160,
+ 161,
+ 162,
+ 163,
+ 164,
+ 165,
+ 166,
+ 167,
+ 168,
+ 169,
+ 170,
+ 171,
+ 172,
+ 173,
+ 174,
+ 175,
+ 176,
+ 177,
+ 178,
+ 179,
+ 180,
+ 181,
+ 182,
+ 183,
+ 184,
+ 185,
+ 186,
+ 187,
+ 188,
+ 189,
+ 190,
+ 191,
+ 192,
+ 193,
+ 194,
+ 195,
+ 196,
+ 197,
+ 198,
+ 199,
+ 200,
+ 201,
+ 202,
+ 203,
+ 204,
+ 205,
+ 206,
+ 207,
+ 208,
+ 209,
+ 210,
+ 211,
+ 212,
+ 213,
+ 214,
+ 215,
+ 216,
+ 217,
+ 218,
+ 219,
+ 220,
+ 221,
+ 222,
+ 223,
+ 224,
+ 225,
+ 226,
+ 227,
+ 228,
+ 229,
+ 230,
+ 231,
+ 232,
+ 233,
+ 234,
+ 235,
+ 236,
+ 237,
+ 238,
+ 239,
+ 240,
+ 241,
+ 242,
+ 243,
+ 244,
+ 245,
+ 246,
+ 247,
+ 248,
+ 249,
+ 250,
+ 251,
+ 252,
+ 253,
+ 254,
+ 255,
+ 256,
+ 257,
+ 258,
+ 259,
+ 260,
+ 261,
+ 262,
+ 263,
+ 264,
+ 265,
+ 266,
+ 267,
+ 268,
+ 269,
+ 270,
+ 271,
+ 272,
+ 273,
+ 274,
+ 275,
+ 276,
+ 277,
+ 278,
+ 279,
+ 280,
+ 281,
+ 282,
+ 283,
+ 284,
+ 285,
+ 286,
+ 287,
+ 288,
+ 289,
+ 290,
+ 291,
+ 292,
+ 293,
+ 294,
+ 295,
+ 296,
+ 297,
+ 298,
+ 299,
+ 300,
+ 301,
+ 302,
+ 303,
+ 304,
+ 305,
+ 306,
+ 307,
+ 308,
+ 309,
+ 310,
+ 311,
+ 312,
+ 313,
+ 314,
+ 315,
+ 316,
+ 317,
+ 318,
+ 319,
+ 320,
+ 321,
+ 322,
+ 323,
+ 324,
+ 325,
+ 326,
+ 327,
+ 328,
+ 329,
+ 330,
+ 331,
+ 332,
+ 333,
+ 334,
+ 335,
+ 336,
+ 337,
+ 338,
+ 339,
+ 340,
+ 341,
+ 342,
+ 343,
+ 344,
+ 345,
+ 346,
+ 347,
+ 348,
+ 349,
+ 350,
+ 351,
+ 352,
+ 353,
+ 354,
+ 355,
+ 356,
+ 357,
+ 358,
+ 359,
+ 360,
+ 361,
+ 362,
+ 363,
+ 364,
+ 365,
+ 366,
+ 367,
+ 368,
+ 369,
+ 370,
+ 371,
+ 372,
+ 373,
+ 374,
+ 375,
+ 376,
+ 377,
+ 378,
+ 379,
+ 380,
+ 381,
+ 382,
+ 383,
+ 384,
+ 385,
+ 386,
+ 387,
+ 388,
+ 389,
+ 390,
+ 391,
+ 392,
+ 393,
+ 394,
+ 395,
+ 396,
+ 397,
+ 398,
+ 399,
+ 400,
+ 401,
+ 402,
+ 403,
+ 404,
+ 405,
+ 406,
+ 407,
+ 408,
+ 409,
+ 410,
+ 411,
+ 412,
+ 413,
+ 414,
+ 415,
+ 416,
+ 417,
+ 418,
+ 419,
+ 420,
+ 421,
+ 422,
+ 423,
+ 424,
+ 425,
+ 426,
+ 427,
+ 428,
+ 429,
+ 430,
+ 431,
+ 432,
+ 433,
+ 434,
+ 435,
+ 436,
+ 437,
+ 438,
+ 439,
+ 440,
+ 441,
+ 442,
+ 443,
+ 444,
+ 445,
+ 446,
+ 447,
+ 448,
+ 449,
+ 450,
+ 451,
+ 452,
+ 453,
+ 454,
+ 455,
+ 456,
+ 457,
+ 458,
+ 459,
+ 460,
+ 461,
+ 462,
+ 463,
+ 464,
+ 465,
+ 466,
+ 467,
+ 468,
+ 469,
+ 470,
+ 471,
+ 472,
+ 473,
+ 474,
+ 475,
+ 476,
+ 477,
+ 478,
+ 479,
+ 480,
+ 481,
+ 482,
+ 483,
+ 484,
+ 485,
+ 486,
+ 487,
+ 488,
+ 489,
+ 490,
+ 491,
+ 492,
+ 493,
+ 494,
+ 495,
+ 496,
+ 497,
+ 498,
+ 499
+ ],
+ "xaxis": "x",
+ "y": [
+ 1,
+ 0.99,
+ 0.9801,
+ 0.970299,
+ 0.96059601,
+ 0.9509900499,
+ 0.941480149401,
+ 0.93206534790699,
+ 0.92274469442792,
+ 0.9135172474836408,
+ 0.9043820750088044,
+ 0.8953382542587163,
+ 0.8863848717161291,
+ 0.8775210229989678,
+ 0.8687458127689781,
+ 0.8600583546412883,
+ 0.8514577710948754,
+ 0.8429431933839266,
+ 0.8345137614500874,
+ 0.8261686238355865,
+ 0.8179069375972307,
+ 0.8097278682212583,
+ 0.8016305895390458,
+ 0.7936142836436553,
+ 0.7856781408072188,
+ 0.7778213593991465,
+ 0.7700431458051551,
+ 0.7623427143471035,
+ 0.7547192872036325,
+ 0.7471720943315961,
+ 0.7397003733882802,
+ 0.7323033696543974,
+ 0.7249803359578534,
+ 0.7177305325982748,
+ 0.7105532272722921,
+ 0.7034476949995692,
+ 0.6964132180495735,
+ 0.6894490858690777,
+ 0.682554595010387,
+ 0.6757290490602831,
+ 0.6689717585696803,
+ 0.6622820409839835,
+ 0.6556592205741436,
+ 0.6491026283684022,
+ 0.6426116020847181,
+ 0.6361854860638709,
+ 0.6298236312032323,
+ 0.6235253948912,
+ 0.617290140942288,
+ 0.6111172395328651,
+ 0.6050060671375365,
+ 0.5989560064661611,
+ 0.5929664464014994,
+ 0.5870367819374844,
+ 0.5811664141181095,
+ 0.5753547499769285,
+ 0.5696012024771592,
+ 0.5639051904523876,
+ 0.5582661385478638,
+ 0.5526834771623851,
+ 0.5471566423907612,
+ 0.5416850759668536,
+ 0.536268225207185,
+ 0.5309055429551132,
+ 0.525596487525562,
+ 0.5203405226503064,
+ 0.5151371174238033,
+ 0.5099857462495653,
+ 0.5048858887870696,
+ 0.4998370298991989,
+ 0.49483865960020695,
+ 0.4898902730042049,
+ 0.4849913702741629,
+ 0.4801414565714212,
+ 0.475340042005707,
+ 0.47058664158564995,
+ 0.4658807751697934,
+ 0.4612219674180955,
+ 0.45660974774391455,
+ 0.4520436502664754,
+ 0.44752321376381066,
+ 0.44304798162617254,
+ 0.4386175018099108,
+ 0.4342313267918117,
+ 0.4298890135238936,
+ 0.42559012338865465,
+ 0.4213342221547681,
+ 0.4171208799332205,
+ 0.41294967113388825,
+ 0.40882017442254937,
+ 0.4047319726783239,
+ 0.40068465295154065,
+ 0.39667780642202527,
+ 0.392711028357805,
+ 0.38878391807422696,
+ 0.3848960788934847,
+ 0.38104711810454983,
+ 0.37723664692350434,
+ 0.37346428045426927,
+ 0.36972963764972655,
+ 0.36603234127322926,
+ 0.36237201786049694,
+ 0.358748297681892,
+ 0.35516081470507305,
+ 0.3516092065580223,
+ 0.34809311449244207,
+ 0.34461218334751764,
+ 0.34116606151404244,
+ 0.337754400898902,
+ 0.334376856889913,
+ 0.33103308832101386,
+ 0.3277227574378037,
+ 0.3244455298634257,
+ 0.3212010745647914,
+ 0.3179890638191435,
+ 0.31480917318095203,
+ 0.3116610814491425,
+ 0.30854447063465107,
+ 0.30545902592830454,
+ 0.3024044356690215,
+ 0.29938039131233124,
+ 0.2963865873992079,
+ 0.29342272152521587,
+ 0.2904884943099637,
+ 0.28758360936686406,
+ 0.2847077732731954,
+ 0.28186069554046345,
+ 0.2790420885850588,
+ 0.2762516676992082,
+ 0.27348915102221616,
+ 0.270754259511994,
+ 0.26804671691687404,
+ 0.2653662497477053,
+ 0.2627125872502282,
+ 0.2600854613777259,
+ 0.2574846067639487,
+ 0.2549097606963092,
+ 0.2523606630893461,
+ 0.24983705645845267,
+ 0.24733868589386815,
+ 0.24486529903492943,
+ 0.24241664604458016,
+ 0.23999247958413436,
+ 0.23759255478829305,
+ 0.2352166292404101,
+ 0.232864462948006,
+ 0.23053581831852593,
+ 0.22823046013534068,
+ 0.22594815553398728,
+ 0.22368867397864745,
+ 0.22145178723886097,
+ 0.2192372693664723,
+ 0.2170448966728076,
+ 0.21487444770607952,
+ 0.2127257032290187,
+ 0.21059844619672857,
+ 0.20849246173476127,
+ 0.2064075371174137,
+ 0.20434346174623952,
+ 0.20230002712877712,
+ 0.20027702685748935,
+ 0.19827425658891443,
+ 0.1962915140230253,
+ 0.19432859888279505,
+ 0.1923853128939671,
+ 0.19046145976502743,
+ 0.1885568451673771,
+ 0.18667127671570335,
+ 0.18480456394854633,
+ 0.18295651830906087,
+ 0.18112695312597027,
+ 0.17931568359471056,
+ 0.17752252675876343,
+ 0.17574730149117582,
+ 0.17398982847626407,
+ 0.17224993019150142,
+ 0.1705274308895864,
+ 0.16882215658069055,
+ 0.16713393501488363,
+ 0.1654625956647348,
+ 0.16380796970808745,
+ 0.16216989001100657,
+ 0.1605481911108965,
+ 0.15894270919978754,
+ 0.15735328210778965,
+ 0.15577974928671176,
+ 0.15422195179384465,
+ 0.1526797322759062,
+ 0.15115293495314713,
+ 0.14964140560361566,
+ 0.1481449915475795,
+ 0.1466635416321037,
+ 0.14519690621578268,
+ 0.14374493715362485,
+ 0.1423074877820886,
+ 0.1408844129042677,
+ 0.13947556877522502,
+ 0.13808081308747278,
+ 0.13670000495659804,
+ 0.13533300490703207,
+ 0.13397967485796175,
+ 0.13263987810938213,
+ 0.1313134793282883,
+ 0.13000034453500542,
+ 0.12870034108965536,
+ 0.1274133376787588,
+ 0.12613920430197123,
+ 0.12487781225895152,
+ 0.123629034136362,
+ 0.12239274379499838,
+ 0.1211688163570484,
+ 0.11995712819347792,
+ 0.11875755691154316,
+ 0.11756998134242772,
+ 0.11639428152900344,
+ 0.1152303387137134,
+ 0.11407803532657627,
+ 0.11293725497331052,
+ 0.1118078824235774,
+ 0.11068980359934164,
+ 0.10958290556334822,
+ 0.10848707650771476,
+ 0.1074022057426376,
+ 0.10632818368521124,
+ 0.10526490184835911,
+ 0.10421225282987552,
+ 0.10317013030157676,
+ 0.102138428998561,
+ 0.10111704470857538,
+ 0.10010587426148965,
+ 0.09910481551887472,
+ 0.098113767363686,
+ 0.09713262969004911,
+ 0.09616130339314864,
+ 0.09519969035921716,
+ 0.09424769345562498,
+ 0.09330521652106873,
+ 0.09237216435585804,
+ 0.09144844271229946,
+ 0.09053395828517646,
+ 0.08962861870232469,
+ 0.08873233251530144,
+ 0.08784500919014843,
+ 0.08696655909824694,
+ 0.08609689350726446,
+ 0.08523592457219181,
+ 0.0843835653264699,
+ 0.0835397296732052,
+ 0.08270433237647315,
+ 0.08187728905270841,
+ 0.08105851616218133,
+ 0.08024793100055952,
+ 0.07944545169055392,
+ 0.07865099717364837,
+ 0.07786448720191189,
+ 0.07708584232989277,
+ 0.07631498390659384,
+ 0.07555183406752791,
+ 0.07479631572685264,
+ 0.07404835256958411,
+ 0.07330786904388827,
+ 0.07257479035344938,
+ 0.07184904244991488,
+ 0.07113055202541574,
+ 0.07041924650516158,
+ 0.06971505404010997,
+ 0.06901790349970886,
+ 0.06832772446471178,
+ 0.06764444722006466,
+ 0.066968002747864,
+ 0.06629832272038537,
+ 0.06563533949318151,
+ 0.06497898609824969,
+ 0.0643291962372672,
+ 0.06368590427489453,
+ 0.06304904523214558,
+ 0.06241855477982412,
+ 0.06179436923202588,
+ 0.06117642553970562,
+ 0.06056466128430856,
+ 0.05995901467146548,
+ 0.05935942452475082,
+ 0.058765830279503314,
+ 0.05817817197670828,
+ 0.057596390256941195,
+ 0.05702042635437178,
+ 0.05645022209082806,
+ 0.05588571986991978,
+ 0.055326862671220584,
+ 0.05477359404450838,
+ 0.054225858104063294,
+ 0.05368359952302266,
+ 0.053146763527792434,
+ 0.052615295892514506,
+ 0.05208914293358936,
+ 0.05156825150425347,
+ 0.051052568989210935,
+ 0.05054204329931883,
+ 0.05003662286632564,
+ 0.04953625663766238,
+ 0.04904089407128576,
+ 0.0485504851305729,
+ 0.048064980279267165,
+ 0.04758433047647449,
+ 0.04710848717170975,
+ 0.04663740229999265,
+ 0.04617102827699272,
+ 0.045709317994222794,
+ 0.04525222481428057,
+ 0.04479970256613776,
+ 0.04435170554047638,
+ 0.043908188485071616,
+ 0.0434691066002209,
+ 0.04303441553421869,
+ 0.0426040713788765,
+ 0.04217803066508773,
+ 0.04175625035843686,
+ 0.041338687854852486,
+ 0.04092530097630396,
+ 0.04051604796654091,
+ 0.04011088748687551,
+ 0.03970977861200675,
+ 0.03931268082588668,
+ 0.03891955401762781,
+ 0.03853035847745153,
+ 0.03814505489267701,
+ 0.03776360434375024,
+ 0.03738596830031274,
+ 0.03701210861730961,
+ 0.03664198753113651,
+ 0.036275567655825146,
+ 0.03591281197926689,
+ 0.035553683859474224,
+ 0.03519814702087948,
+ 0.03484616555067068,
+ 0.034497703895163975,
+ 0.03415272685621234,
+ 0.03381119958765021,
+ 0.03347308759177371,
+ 0.03313835671585597,
+ 0.03280697314869741,
+ 0.032478903417210436,
+ 0.032154114383038335,
+ 0.03183257323920795,
+ 0.03151424750681587,
+ 0.03119910503174771,
+ 0.030887113981430233,
+ 0.03057824284161593,
+ 0.03027246041319977,
+ 0.029969735809067772,
+ 0.029670038450977095,
+ 0.029373338066467324,
+ 0.02907960468580265,
+ 0.028788808638944625,
+ 0.028500920552555178,
+ 0.028215911347029624,
+ 0.027933752233559327,
+ 0.027654414711223735,
+ 0.027377870564111496,
+ 0.027104091858470385,
+ 0.026833050939885677,
+ 0.02656472043048682,
+ 0.02629907322618195,
+ 0.026036082493920133,
+ 0.02577572166898093,
+ 0.02551796445229112,
+ 0.02526278480776821,
+ 0.025010156959690527,
+ 0.02476005539009362,
+ 0.024512454836192684,
+ 0.024267330287830756,
+ 0.024024656984952448,
+ 0.023784410415102923,
+ 0.023546566310951898,
+ 0.023311100647842375,
+ 0.02307798964136395,
+ 0.022847209744950314,
+ 0.02261873764750081,
+ 0.022392550271025803,
+ 0.022168624768315544,
+ 0.02194693852063239,
+ 0.021727469135426065,
+ 0.021510194444071803,
+ 0.021295092499631085,
+ 0.021082141574634772,
+ 0.020871320158888425,
+ 0.020662606957299545,
+ 0.020455980887726547,
+ 0.02025142107884928,
+ 0.020048906868060788,
+ 0.01984841779938018,
+ 0.019649933621386374,
+ 0.019453434285172513,
+ 0.019258899942320787,
+ 0.01906631094289758,
+ 0.0188756478334686,
+ 0.018686891355133916,
+ 0.018500022441582577,
+ 0.01831502221716675,
+ 0.018131871994995084,
+ 0.017950553275045134,
+ 0.017771047742294682,
+ 0.017593337264871736,
+ 0.01741740389222302,
+ 0.01724322985330079,
+ 0.017070797554767782,
+ 0.016900089579220106,
+ 0.016731088683427906,
+ 0.016563777796593626,
+ 0.016398140018627688,
+ 0.01623415861844141,
+ 0.016071817032256998,
+ 0.01591109886193443,
+ 0.015751987873315085,
+ 0.015594467994581937,
+ 0.015438523314636117,
+ 0.015284138081489752,
+ 0.015131296700674856,
+ 0.014979983733668108,
+ 0.014830183896331426,
+ 0.014681882057368112,
+ 0.01453506323679443,
+ 0.014389712604426484,
+ 0.01424581547838222,
+ 0.014103357323598397,
+ 0.013962323750362412,
+ 0.013822700512858789,
+ 0.0136844735077302,
+ 0.0135476287726529,
+ 0.01341215248492637,
+ 0.013278030960077106,
+ 0.013145250650476337,
+ 0.01301379814397157,
+ 0.012883660162531854,
+ 0.012754823560906537,
+ 0.01262727532529747,
+ 0.012501002572044496,
+ 0.01237599254632405,
+ 0.01225223262086081,
+ 0.012129710294652202,
+ 0.01200841319170568,
+ 0.011888329059788623,
+ 0.011769445769190735,
+ 0.01165175131149883,
+ 0.011535233798383842,
+ 0.011419881460400004,
+ 0.011305682645796004,
+ 0.011192625819338045,
+ 0.011080699561144665,
+ 0.010969892565533218,
+ 0.010860193639877886,
+ 0.010751591703479106,
+ 0.010644075786444315,
+ 0.010537635028579871,
+ 0.010432258678294072,
+ 0.010327936091511131,
+ 0.010224656730596022,
+ 0.01012241016329006,
+ 0.01002118606165716,
+ 0.009920974201040588,
+ 0.009821764459030182,
+ 0.00972354681443988,
+ 0.00962631134629548,
+ 0.009530048232832523,
+ 0.0094347477505042,
+ 0.009340400272999157,
+ 0.009246996270269163,
+ 0.009154526307566474,
+ 0.009062981044490808,
+ 0.0089723512340459,
+ 0.00888262772170544,
+ 0.008793801444488386,
+ 0.008705863430043502,
+ 0.008618804795743068,
+ 0.008532616747785637,
+ 0.00844729058030778,
+ 0.008362817674504702,
+ 0.008279189497759654,
+ 0.008196397602782058,
+ 0.008114433626754238,
+ 0.008033289290486696,
+ 0.00795295639758183,
+ 0.00787342683360601,
+ 0.007794692565269949,
+ 0.00771674563961725,
+ 0.007639578183221077,
+ 0.007563182401388867,
+ 0.007487550577374978,
+ 0.007412675071601228,
+ 0.007338548320885215,
+ 0.0072651628376763635,
+ 0.0071925112092996,
+ 0.007120586097206604,
+ 0.007049380236234538,
+ 0.006978886433872193,
+ 0.006909097569533471,
+ 0.006840006593838137,
+ 0.0067716065278997555,
+ 0.006703890462620758,
+ 0.006636851557994551
+ ],
+ "yaxis": "y2"
+ },
+ {
+ "mode": "lines",
+ "name": "Score",
+ "type": "scatter",
+ "x": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 60,
+ 61,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 81,
+ 82,
+ 83,
+ 84,
+ 85,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 91,
+ 92,
+ 93,
+ 94,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 108,
+ 109,
+ 110,
+ 111,
+ 112,
+ 113,
+ 114,
+ 115,
+ 116,
+ 117,
+ 118,
+ 119,
+ 120,
+ 121,
+ 122,
+ 123,
+ 124,
+ 125,
+ 126,
+ 127,
+ 128,
+ 129,
+ 130,
+ 131,
+ 132,
+ 133,
+ 134,
+ 135,
+ 136,
+ 137,
+ 138,
+ 139,
+ 140,
+ 141,
+ 142,
+ 143,
+ 144,
+ 145,
+ 146,
+ 147,
+ 148,
+ 149,
+ 150,
+ 151,
+ 152,
+ 153,
+ 154,
+ 155,
+ 156,
+ 157,
+ 158,
+ 159,
+ 160,
+ 161,
+ 162,
+ 163,
+ 164,
+ 165,
+ 166,
+ 167,
+ 168,
+ 169,
+ 170,
+ 171,
+ 172,
+ 173,
+ 174,
+ 175,
+ 176,
+ 177,
+ 178,
+ 179,
+ 180,
+ 181,
+ 182,
+ 183,
+ 184,
+ 185,
+ 186,
+ 187,
+ 188,
+ 189,
+ 190,
+ 191,
+ 192,
+ 193,
+ 194,
+ 195,
+ 196,
+ 197,
+ 198,
+ 199,
+ 200,
+ 201,
+ 202,
+ 203,
+ 204,
+ 205,
+ 206,
+ 207,
+ 208,
+ 209,
+ 210,
+ 211,
+ 212,
+ 213,
+ 214,
+ 215,
+ 216,
+ 217,
+ 218,
+ 219,
+ 220,
+ 221,
+ 222,
+ 223,
+ 224,
+ 225,
+ 226,
+ 227,
+ 228,
+ 229,
+ 230,
+ 231,
+ 232,
+ 233,
+ 234,
+ 235,
+ 236,
+ 237,
+ 238,
+ 239,
+ 240,
+ 241,
+ 242,
+ 243,
+ 244,
+ 245,
+ 246,
+ 247,
+ 248,
+ 249,
+ 250,
+ 251,
+ 252,
+ 253,
+ 254,
+ 255,
+ 256,
+ 257,
+ 258,
+ 259,
+ 260,
+ 261,
+ 262,
+ 263,
+ 264,
+ 265,
+ 266,
+ 267,
+ 268,
+ 269,
+ 270,
+ 271,
+ 272,
+ 273,
+ 274,
+ 275,
+ 276,
+ 277,
+ 278,
+ 279,
+ 280,
+ 281,
+ 282,
+ 283,
+ 284,
+ 285,
+ 286,
+ 287,
+ 288,
+ 289,
+ 290,
+ 291,
+ 292,
+ 293,
+ 294,
+ 295,
+ 296,
+ 297,
+ 298,
+ 299,
+ 300,
+ 301,
+ 302,
+ 303,
+ 304,
+ 305,
+ 306,
+ 307,
+ 308,
+ 309,
+ 310,
+ 311,
+ 312,
+ 313,
+ 314,
+ 315,
+ 316,
+ 317,
+ 318,
+ 319,
+ 320,
+ 321,
+ 322,
+ 323,
+ 324,
+ 325,
+ 326,
+ 327,
+ 328,
+ 329,
+ 330,
+ 331,
+ 332,
+ 333,
+ 334,
+ 335,
+ 336,
+ 337,
+ 338,
+ 339,
+ 340,
+ 341,
+ 342,
+ 343,
+ 344,
+ 345,
+ 346,
+ 347,
+ 348,
+ 349,
+ 350,
+ 351,
+ 352,
+ 353,
+ 354,
+ 355,
+ 356,
+ 357,
+ 358,
+ 359,
+ 360,
+ 361,
+ 362,
+ 363,
+ 364,
+ 365,
+ 366,
+ 367,
+ 368,
+ 369,
+ 370,
+ 371,
+ 372,
+ 373,
+ 374,
+ 375,
+ 376,
+ 377,
+ 378,
+ 379,
+ 380,
+ 381,
+ 382,
+ 383,
+ 384,
+ 385,
+ 386,
+ 387,
+ 388,
+ 389,
+ 390,
+ 391,
+ 392,
+ 393,
+ 394,
+ 395,
+ 396,
+ 397,
+ 398,
+ 399,
+ 400,
+ 401,
+ 402,
+ 403,
+ 404,
+ 405,
+ 406,
+ 407,
+ 408,
+ 409,
+ 410,
+ 411,
+ 412,
+ 413,
+ 414,
+ 415,
+ 416,
+ 417,
+ 418,
+ 419,
+ 420,
+ 421,
+ 422,
+ 423,
+ 424,
+ 425,
+ 426,
+ 427,
+ 428,
+ 429,
+ 430,
+ 431,
+ 432,
+ 433,
+ 434,
+ 435,
+ 436,
+ 437,
+ 438,
+ 439,
+ 440,
+ 441,
+ 442,
+ 443,
+ 444,
+ 445,
+ 446,
+ 447,
+ 448,
+ 449,
+ 450,
+ 451,
+ 452,
+ 453,
+ 454,
+ 455,
+ 456,
+ 457,
+ 458,
+ 459,
+ 460,
+ 461,
+ 462,
+ 463,
+ 464,
+ 465,
+ 466,
+ 467,
+ 468,
+ 469,
+ 470,
+ 471,
+ 472,
+ 473,
+ 474,
+ 475,
+ 476,
+ 477,
+ 478,
+ 479,
+ 480,
+ 481,
+ 482,
+ 483,
+ 484,
+ 485,
+ 486,
+ 487,
+ 488,
+ 489,
+ 490,
+ 491,
+ 492,
+ 493,
+ 494,
+ 495,
+ 496,
+ 497,
+ 498,
+ 499
+ ],
+ "xaxis": "x",
+ "y": [
+ 9,
+ 20,
+ 19,
+ 16,
+ 26,
+ 29,
+ 71,
+ 21,
+ 33,
+ 9,
+ 19,
+ 33,
+ 14,
+ 15,
+ 19,
+ 34,
+ 14,
+ 32,
+ 11,
+ 17,
+ 36,
+ 21,
+ 39,
+ 21,
+ 17,
+ 43,
+ 34,
+ 17,
+ 15,
+ 14,
+ 23,
+ 15,
+ 10,
+ 11,
+ 13,
+ 18,
+ 14,
+ 20,
+ 19,
+ 23,
+ 69,
+ 47,
+ 12,
+ 54,
+ 54,
+ 29,
+ 19,
+ 16,
+ 21,
+ 15,
+ 18,
+ 21,
+ 21,
+ 41,
+ 28,
+ 16,
+ 63,
+ 37,
+ 57,
+ 26,
+ 16,
+ 49,
+ 46,
+ 78,
+ 57,
+ 36,
+ 41,
+ 22,
+ 23,
+ 30,
+ 63,
+ 27,
+ 9,
+ 38,
+ 29,
+ 72,
+ 26,
+ 63,
+ 64,
+ 68,
+ 37,
+ 27,
+ 100,
+ 100,
+ 51,
+ 100,
+ 88,
+ 40,
+ 100,
+ 28,
+ 100,
+ 68,
+ 33,
+ 66,
+ 26,
+ 100,
+ 78,
+ 48,
+ 57,
+ 28,
+ 95,
+ 85,
+ 40,
+ 52,
+ 53,
+ 89,
+ 69,
+ 100,
+ 64,
+ 100,
+ 100,
+ 90,
+ 100,
+ 100,
+ 70,
+ 16,
+ 77,
+ 29,
+ 85,
+ 56,
+ 100,
+ 100,
+ 100,
+ 100,
+ 33,
+ 100,
+ 42,
+ 100,
+ 100,
+ 46,
+ 95,
+ 100,
+ 100,
+ 100,
+ 100,
+ 62,
+ 100,
+ 65,
+ 57,
+ 61,
+ 69,
+ 59,
+ 76,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 78,
+ 61,
+ 70,
+ 43,
+ 100,
+ 92,
+ 100,
+ 78,
+ 98,
+ 70,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 93,
+ 93,
+ 90,
+ 100,
+ 100,
+ 100,
+ 55,
+ 100,
+ 80,
+ 80,
+ 64,
+ 100,
+ 24,
+ 57,
+ 100,
+ 100,
+ 37,
+ 100,
+ 100,
+ 53,
+ 100,
+ 100,
+ 75,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 49,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 75,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 94,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 89,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 94,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 89,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 34,
+ 100,
+ 100,
+ 60,
+ 100,
+ 53,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 46,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 88,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 93,
+ 100,
+ 100,
+ 100,
+ 78,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 70,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 44,
+ 100,
+ 100,
+ 66,
+ 100,
+ 44,
+ 85,
+ 100,
+ 100,
+ 53,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 92,
+ 100,
+ 100,
+ 98,
+ 100,
+ 40,
+ 100,
+ 80,
+ 100,
+ 26,
+ 23,
+ 66,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 93,
+ 100,
+ 84,
+ 100,
+ 52,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100
+ ],
+ "yaxis": "y"
+ }
+ ],
+ "layout": {
+ "template": {
+ "data": {
+ "bar": [
+ {
+ "error_x": {
+ "color": "#2a3f5f"
+ },
+ "error_y": {
+ "color": "#2a3f5f"
+ },
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "bar"
+ }
+ ],
+ "barpolar": [
+ {
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "barpolar"
+ }
+ ],
+ "carpet": [
+ {
+ "aaxis": {
+ "endlinecolor": "#2a3f5f",
+ "gridcolor": "white",
+ "linecolor": "white",
+ "minorgridcolor": "white",
+ "startlinecolor": "#2a3f5f"
+ },
+ "baxis": {
+ "endlinecolor": "#2a3f5f",
+ "gridcolor": "white",
+ "linecolor": "white",
+ "minorgridcolor": "white",
+ "startlinecolor": "#2a3f5f"
+ },
+ "type": "carpet"
+ }
+ ],
+ "choropleth": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "choropleth"
+ }
+ ],
+ "contour": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "contour"
+ }
+ ],
+ "contourcarpet": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "contourcarpet"
+ }
+ ],
+ "heatmap": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "heatmap"
+ }
+ ],
+ "heatmapgl": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "heatmapgl"
+ }
+ ],
+ "histogram": [
+ {
+ "marker": {
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "histogram"
+ }
+ ],
+ "histogram2d": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "histogram2d"
+ }
+ ],
+ "histogram2dcontour": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "histogram2dcontour"
+ }
+ ],
+ "mesh3d": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "mesh3d"
+ }
+ ],
+ "parcoords": [
+ {
+ "line": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "parcoords"
+ }
+ ],
+ "pie": [
+ {
+ "automargin": true,
+ "type": "pie"
+ }
+ ],
+ "scatter": [
+ {
+ "fillpattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ },
+ "type": "scatter"
+ }
+ ],
+ "scatter3d": [
+ {
+ "line": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatter3d"
+ }
+ ],
+ "scattercarpet": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattercarpet"
+ }
+ ],
+ "scattergeo": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattergeo"
+ }
+ ],
+ "scattergl": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattergl"
+ }
+ ],
+ "scattermapbox": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattermapbox"
+ }
+ ],
+ "scatterpolar": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterpolar"
+ }
+ ],
+ "scatterpolargl": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterpolargl"
+ }
+ ],
+ "scatterternary": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterternary"
+ }
+ ],
+ "surface": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "surface"
+ }
+ ],
+ "table": [
+ {
+ "cells": {
+ "fill": {
+ "color": "#EBF0F8"
+ },
+ "line": {
+ "color": "white"
+ }
+ },
+ "header": {
+ "fill": {
+ "color": "#C8D4E3"
+ },
+ "line": {
+ "color": "white"
+ }
+ },
+ "type": "table"
+ }
+ ]
+ },
+ "layout": {
+ "annotationdefaults": {
+ "arrowcolor": "#2a3f5f",
+ "arrowhead": 0,
+ "arrowwidth": 1
+ },
+ "autotypenumbers": "strict",
+ "coloraxis": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "colorscale": {
+ "diverging": [
+ [
+ 0,
+ "#8e0152"
+ ],
+ [
+ 0.1,
+ "#c51b7d"
+ ],
+ [
+ 0.2,
+ "#de77ae"
+ ],
+ [
+ 0.3,
+ "#f1b6da"
+ ],
+ [
+ 0.4,
+ "#fde0ef"
+ ],
+ [
+ 0.5,
+ "#f7f7f7"
+ ],
+ [
+ 0.6,
+ "#e6f5d0"
+ ],
+ [
+ 0.7,
+ "#b8e186"
+ ],
+ [
+ 0.8,
+ "#7fbc41"
+ ],
+ [
+ 0.9,
+ "#4d9221"
+ ],
+ [
+ 1,
+ "#276419"
+ ]
+ ],
+ "sequential": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "sequentialminus": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ]
+ },
+ "colorway": [
+ "#636efa",
+ "#EF553B",
+ "#00cc96",
+ "#ab63fa",
+ "#FFA15A",
+ "#19d3f3",
+ "#FF6692",
+ "#B6E880",
+ "#FF97FF",
+ "#FECB52"
+ ],
+ "font": {
+ "color": "#2a3f5f"
+ },
+ "geo": {
+ "bgcolor": "white",
+ "lakecolor": "white",
+ "landcolor": "#E5ECF6",
+ "showlakes": true,
+ "showland": true,
+ "subunitcolor": "white"
+ },
+ "hoverlabel": {
+ "align": "left"
+ },
+ "hovermode": "closest",
+ "mapbox": {
+ "style": "light"
+ },
+ "paper_bgcolor": "white",
+ "plot_bgcolor": "#E5ECF6",
+ "polar": {
+ "angularaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "radialaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "scene": {
+ "xaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "yaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "zaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "caxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "DDQN Training, Advantage Type: AdvantageType.MAX"
+ },
+ "xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 0.94
+ ],
+ "title": {
+ "text": "Episode"
+ }
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Score"
+ }
+ },
+ "yaxis2": {
+ "anchor": "x",
+ "overlaying": "y",
+ "side": "right",
+ "title": {
+ "text": "Exploration Rate"
+ }
+ }
+ }
+ },
+ "image/png": "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",
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "797cbc942554461f82c84ff0d5c80f6a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/500 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "mode": "lines",
+ "name": "Exploration Rate",
+ "type": "scatter",
+ "x": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 60,
+ 61,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 81,
+ 82,
+ 83,
+ 84,
+ 85,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 91,
+ 92,
+ 93,
+ 94,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 108,
+ 109,
+ 110,
+ 111,
+ 112,
+ 113,
+ 114,
+ 115,
+ 116,
+ 117,
+ 118,
+ 119,
+ 120,
+ 121,
+ 122,
+ 123,
+ 124,
+ 125,
+ 126,
+ 127,
+ 128,
+ 129,
+ 130,
+ 131,
+ 132,
+ 133,
+ 134,
+ 135,
+ 136,
+ 137,
+ 138,
+ 139,
+ 140,
+ 141,
+ 142,
+ 143,
+ 144,
+ 145,
+ 146,
+ 147,
+ 148,
+ 149,
+ 150,
+ 151,
+ 152,
+ 153,
+ 154,
+ 155,
+ 156,
+ 157,
+ 158,
+ 159,
+ 160,
+ 161,
+ 162,
+ 163,
+ 164,
+ 165,
+ 166,
+ 167,
+ 168,
+ 169,
+ 170,
+ 171,
+ 172,
+ 173,
+ 174,
+ 175,
+ 176,
+ 177,
+ 178,
+ 179,
+ 180,
+ 181,
+ 182,
+ 183,
+ 184,
+ 185,
+ 186,
+ 187,
+ 188,
+ 189,
+ 190,
+ 191,
+ 192,
+ 193,
+ 194,
+ 195,
+ 196,
+ 197,
+ 198,
+ 199,
+ 200,
+ 201,
+ 202,
+ 203,
+ 204,
+ 205,
+ 206,
+ 207,
+ 208,
+ 209,
+ 210,
+ 211,
+ 212,
+ 213,
+ 214,
+ 215,
+ 216,
+ 217,
+ 218,
+ 219,
+ 220,
+ 221,
+ 222,
+ 223,
+ 224,
+ 225,
+ 226,
+ 227,
+ 228,
+ 229,
+ 230,
+ 231,
+ 232,
+ 233,
+ 234,
+ 235,
+ 236,
+ 237,
+ 238,
+ 239,
+ 240,
+ 241,
+ 242,
+ 243,
+ 244,
+ 245,
+ 246,
+ 247,
+ 248,
+ 249,
+ 250,
+ 251,
+ 252,
+ 253,
+ 254,
+ 255,
+ 256,
+ 257,
+ 258,
+ 259,
+ 260,
+ 261,
+ 262,
+ 263,
+ 264,
+ 265,
+ 266,
+ 267,
+ 268,
+ 269,
+ 270,
+ 271,
+ 272,
+ 273,
+ 274,
+ 275,
+ 276,
+ 277,
+ 278,
+ 279,
+ 280,
+ 281,
+ 282,
+ 283,
+ 284,
+ 285,
+ 286,
+ 287,
+ 288,
+ 289,
+ 290,
+ 291,
+ 292,
+ 293,
+ 294,
+ 295,
+ 296,
+ 297,
+ 298,
+ 299,
+ 300,
+ 301,
+ 302,
+ 303,
+ 304,
+ 305,
+ 306,
+ 307,
+ 308,
+ 309,
+ 310,
+ 311,
+ 312,
+ 313,
+ 314,
+ 315,
+ 316,
+ 317,
+ 318,
+ 319,
+ 320,
+ 321,
+ 322,
+ 323,
+ 324,
+ 325,
+ 326,
+ 327,
+ 328,
+ 329,
+ 330,
+ 331,
+ 332,
+ 333,
+ 334,
+ 335,
+ 336,
+ 337,
+ 338,
+ 339,
+ 340,
+ 341,
+ 342,
+ 343,
+ 344,
+ 345,
+ 346,
+ 347,
+ 348,
+ 349,
+ 350,
+ 351,
+ 352,
+ 353,
+ 354,
+ 355,
+ 356,
+ 357,
+ 358,
+ 359,
+ 360,
+ 361,
+ 362,
+ 363,
+ 364,
+ 365,
+ 366,
+ 367,
+ 368,
+ 369,
+ 370,
+ 371,
+ 372,
+ 373,
+ 374,
+ 375,
+ 376,
+ 377,
+ 378,
+ 379,
+ 380,
+ 381,
+ 382,
+ 383,
+ 384,
+ 385,
+ 386,
+ 387,
+ 388,
+ 389,
+ 390,
+ 391,
+ 392,
+ 393,
+ 394,
+ 395,
+ 396,
+ 397,
+ 398,
+ 399,
+ 400,
+ 401,
+ 402,
+ 403,
+ 404,
+ 405,
+ 406,
+ 407,
+ 408,
+ 409,
+ 410,
+ 411,
+ 412,
+ 413,
+ 414,
+ 415,
+ 416,
+ 417,
+ 418,
+ 419,
+ 420,
+ 421,
+ 422,
+ 423,
+ 424,
+ 425,
+ 426,
+ 427,
+ 428,
+ 429,
+ 430,
+ 431,
+ 432,
+ 433,
+ 434,
+ 435,
+ 436,
+ 437,
+ 438,
+ 439,
+ 440,
+ 441,
+ 442,
+ 443,
+ 444,
+ 445,
+ 446,
+ 447,
+ 448,
+ 449,
+ 450,
+ 451,
+ 452,
+ 453,
+ 454,
+ 455,
+ 456,
+ 457,
+ 458,
+ 459,
+ 460,
+ 461,
+ 462,
+ 463,
+ 464,
+ 465,
+ 466,
+ 467,
+ 468,
+ 469,
+ 470,
+ 471,
+ 472,
+ 473,
+ 474,
+ 475,
+ 476,
+ 477,
+ 478,
+ 479,
+ 480,
+ 481,
+ 482,
+ 483,
+ 484,
+ 485,
+ 486,
+ 487,
+ 488,
+ 489,
+ 490,
+ 491,
+ 492,
+ 493,
+ 494,
+ 495,
+ 496,
+ 497,
+ 498,
+ 499
+ ],
+ "xaxis": "x",
+ "y": [
+ 1,
+ 0.99,
+ 0.9801,
+ 0.970299,
+ 0.96059601,
+ 0.9509900499,
+ 0.941480149401,
+ 0.93206534790699,
+ 0.92274469442792,
+ 0.9135172474836408,
+ 0.9043820750088044,
+ 0.8953382542587163,
+ 0.8863848717161291,
+ 0.8775210229989678,
+ 0.8687458127689781,
+ 0.8600583546412883,
+ 0.8514577710948754,
+ 0.8429431933839266,
+ 0.8345137614500874,
+ 0.8261686238355865,
+ 0.8179069375972307,
+ 0.8097278682212583,
+ 0.8016305895390458,
+ 0.7936142836436553,
+ 0.7856781408072188,
+ 0.7778213593991465,
+ 0.7700431458051551,
+ 0.7623427143471035,
+ 0.7547192872036325,
+ 0.7471720943315961,
+ 0.7397003733882802,
+ 0.7323033696543974,
+ 0.7249803359578534,
+ 0.7177305325982748,
+ 0.7105532272722921,
+ 0.7034476949995692,
+ 0.6964132180495735,
+ 0.6894490858690777,
+ 0.682554595010387,
+ 0.6757290490602831,
+ 0.6689717585696803,
+ 0.6622820409839835,
+ 0.6556592205741436,
+ 0.6491026283684022,
+ 0.6426116020847181,
+ 0.6361854860638709,
+ 0.6298236312032323,
+ 0.6235253948912,
+ 0.617290140942288,
+ 0.6111172395328651,
+ 0.6050060671375365,
+ 0.5989560064661611,
+ 0.5929664464014994,
+ 0.5870367819374844,
+ 0.5811664141181095,
+ 0.5753547499769285,
+ 0.5696012024771592,
+ 0.5639051904523876,
+ 0.5582661385478638,
+ 0.5526834771623851,
+ 0.5471566423907612,
+ 0.5416850759668536,
+ 0.536268225207185,
+ 0.5309055429551132,
+ 0.525596487525562,
+ 0.5203405226503064,
+ 0.5151371174238033,
+ 0.5099857462495653,
+ 0.5048858887870696,
+ 0.4998370298991989,
+ 0.49483865960020695,
+ 0.4898902730042049,
+ 0.4849913702741629,
+ 0.4801414565714212,
+ 0.475340042005707,
+ 0.47058664158564995,
+ 0.4658807751697934,
+ 0.4612219674180955,
+ 0.45660974774391455,
+ 0.4520436502664754,
+ 0.44752321376381066,
+ 0.44304798162617254,
+ 0.4386175018099108,
+ 0.4342313267918117,
+ 0.4298890135238936,
+ 0.42559012338865465,
+ 0.4213342221547681,
+ 0.4171208799332205,
+ 0.41294967113388825,
+ 0.40882017442254937,
+ 0.4047319726783239,
+ 0.40068465295154065,
+ 0.39667780642202527,
+ 0.392711028357805,
+ 0.38878391807422696,
+ 0.3848960788934847,
+ 0.38104711810454983,
+ 0.37723664692350434,
+ 0.37346428045426927,
+ 0.36972963764972655,
+ 0.36603234127322926,
+ 0.36237201786049694,
+ 0.358748297681892,
+ 0.35516081470507305,
+ 0.3516092065580223,
+ 0.34809311449244207,
+ 0.34461218334751764,
+ 0.34116606151404244,
+ 0.337754400898902,
+ 0.334376856889913,
+ 0.33103308832101386,
+ 0.3277227574378037,
+ 0.3244455298634257,
+ 0.3212010745647914,
+ 0.3179890638191435,
+ 0.31480917318095203,
+ 0.3116610814491425,
+ 0.30854447063465107,
+ 0.30545902592830454,
+ 0.3024044356690215,
+ 0.29938039131233124,
+ 0.2963865873992079,
+ 0.29342272152521587,
+ 0.2904884943099637,
+ 0.28758360936686406,
+ 0.2847077732731954,
+ 0.28186069554046345,
+ 0.2790420885850588,
+ 0.2762516676992082,
+ 0.27348915102221616,
+ 0.270754259511994,
+ 0.26804671691687404,
+ 0.2653662497477053,
+ 0.2627125872502282,
+ 0.2600854613777259,
+ 0.2574846067639487,
+ 0.2549097606963092,
+ 0.2523606630893461,
+ 0.24983705645845267,
+ 0.24733868589386815,
+ 0.24486529903492943,
+ 0.24241664604458016,
+ 0.23999247958413436,
+ 0.23759255478829305,
+ 0.2352166292404101,
+ 0.232864462948006,
+ 0.23053581831852593,
+ 0.22823046013534068,
+ 0.22594815553398728,
+ 0.22368867397864745,
+ 0.22145178723886097,
+ 0.2192372693664723,
+ 0.2170448966728076,
+ 0.21487444770607952,
+ 0.2127257032290187,
+ 0.21059844619672857,
+ 0.20849246173476127,
+ 0.2064075371174137,
+ 0.20434346174623952,
+ 0.20230002712877712,
+ 0.20027702685748935,
+ 0.19827425658891443,
+ 0.1962915140230253,
+ 0.19432859888279505,
+ 0.1923853128939671,
+ 0.19046145976502743,
+ 0.1885568451673771,
+ 0.18667127671570335,
+ 0.18480456394854633,
+ 0.18295651830906087,
+ 0.18112695312597027,
+ 0.17931568359471056,
+ 0.17752252675876343,
+ 0.17574730149117582,
+ 0.17398982847626407,
+ 0.17224993019150142,
+ 0.1705274308895864,
+ 0.16882215658069055,
+ 0.16713393501488363,
+ 0.1654625956647348,
+ 0.16380796970808745,
+ 0.16216989001100657,
+ 0.1605481911108965,
+ 0.15894270919978754,
+ 0.15735328210778965,
+ 0.15577974928671176,
+ 0.15422195179384465,
+ 0.1526797322759062,
+ 0.15115293495314713,
+ 0.14964140560361566,
+ 0.1481449915475795,
+ 0.1466635416321037,
+ 0.14519690621578268,
+ 0.14374493715362485,
+ 0.1423074877820886,
+ 0.1408844129042677,
+ 0.13947556877522502,
+ 0.13808081308747278,
+ 0.13670000495659804,
+ 0.13533300490703207,
+ 0.13397967485796175,
+ 0.13263987810938213,
+ 0.1313134793282883,
+ 0.13000034453500542,
+ 0.12870034108965536,
+ 0.1274133376787588,
+ 0.12613920430197123,
+ 0.12487781225895152,
+ 0.123629034136362,
+ 0.12239274379499838,
+ 0.1211688163570484,
+ 0.11995712819347792,
+ 0.11875755691154316,
+ 0.11756998134242772,
+ 0.11639428152900344,
+ 0.1152303387137134,
+ 0.11407803532657627,
+ 0.11293725497331052,
+ 0.1118078824235774,
+ 0.11068980359934164,
+ 0.10958290556334822,
+ 0.10848707650771476,
+ 0.1074022057426376,
+ 0.10632818368521124,
+ 0.10526490184835911,
+ 0.10421225282987552,
+ 0.10317013030157676,
+ 0.102138428998561,
+ 0.10111704470857538,
+ 0.10010587426148965,
+ 0.09910481551887472,
+ 0.098113767363686,
+ 0.09713262969004911,
+ 0.09616130339314864,
+ 0.09519969035921716,
+ 0.09424769345562498,
+ 0.09330521652106873,
+ 0.09237216435585804,
+ 0.09144844271229946,
+ 0.09053395828517646,
+ 0.08962861870232469,
+ 0.08873233251530144,
+ 0.08784500919014843,
+ 0.08696655909824694,
+ 0.08609689350726446,
+ 0.08523592457219181,
+ 0.0843835653264699,
+ 0.0835397296732052,
+ 0.08270433237647315,
+ 0.08187728905270841,
+ 0.08105851616218133,
+ 0.08024793100055952,
+ 0.07944545169055392,
+ 0.07865099717364837,
+ 0.07786448720191189,
+ 0.07708584232989277,
+ 0.07631498390659384,
+ 0.07555183406752791,
+ 0.07479631572685264,
+ 0.07404835256958411,
+ 0.07330786904388827,
+ 0.07257479035344938,
+ 0.07184904244991488,
+ 0.07113055202541574,
+ 0.07041924650516158,
+ 0.06971505404010997,
+ 0.06901790349970886,
+ 0.06832772446471178,
+ 0.06764444722006466,
+ 0.066968002747864,
+ 0.06629832272038537,
+ 0.06563533949318151,
+ 0.06497898609824969,
+ 0.0643291962372672,
+ 0.06368590427489453,
+ 0.06304904523214558,
+ 0.06241855477982412,
+ 0.06179436923202588,
+ 0.06117642553970562,
+ 0.06056466128430856,
+ 0.05995901467146548,
+ 0.05935942452475082,
+ 0.058765830279503314,
+ 0.05817817197670828,
+ 0.057596390256941195,
+ 0.05702042635437178,
+ 0.05645022209082806,
+ 0.05588571986991978,
+ 0.055326862671220584,
+ 0.05477359404450838,
+ 0.054225858104063294,
+ 0.05368359952302266,
+ 0.053146763527792434,
+ 0.052615295892514506,
+ 0.05208914293358936,
+ 0.05156825150425347,
+ 0.051052568989210935,
+ 0.05054204329931883,
+ 0.05003662286632564,
+ 0.04953625663766238,
+ 0.04904089407128576,
+ 0.0485504851305729,
+ 0.048064980279267165,
+ 0.04758433047647449,
+ 0.04710848717170975,
+ 0.04663740229999265,
+ 0.04617102827699272,
+ 0.045709317994222794,
+ 0.04525222481428057,
+ 0.04479970256613776,
+ 0.04435170554047638,
+ 0.043908188485071616,
+ 0.0434691066002209,
+ 0.04303441553421869,
+ 0.0426040713788765,
+ 0.04217803066508773,
+ 0.04175625035843686,
+ 0.041338687854852486,
+ 0.04092530097630396,
+ 0.04051604796654091,
+ 0.04011088748687551,
+ 0.03970977861200675,
+ 0.03931268082588668,
+ 0.03891955401762781,
+ 0.03853035847745153,
+ 0.03814505489267701,
+ 0.03776360434375024,
+ 0.03738596830031274,
+ 0.03701210861730961,
+ 0.03664198753113651,
+ 0.036275567655825146,
+ 0.03591281197926689,
+ 0.035553683859474224,
+ 0.03519814702087948,
+ 0.03484616555067068,
+ 0.034497703895163975,
+ 0.03415272685621234,
+ 0.03381119958765021,
+ 0.03347308759177371,
+ 0.03313835671585597,
+ 0.03280697314869741,
+ 0.032478903417210436,
+ 0.032154114383038335,
+ 0.03183257323920795,
+ 0.03151424750681587,
+ 0.03119910503174771,
+ 0.030887113981430233,
+ 0.03057824284161593,
+ 0.03027246041319977,
+ 0.029969735809067772,
+ 0.029670038450977095,
+ 0.029373338066467324,
+ 0.02907960468580265,
+ 0.028788808638944625,
+ 0.028500920552555178,
+ 0.028215911347029624,
+ 0.027933752233559327,
+ 0.027654414711223735,
+ 0.027377870564111496,
+ 0.027104091858470385,
+ 0.026833050939885677,
+ 0.02656472043048682,
+ 0.02629907322618195,
+ 0.026036082493920133,
+ 0.02577572166898093,
+ 0.02551796445229112,
+ 0.02526278480776821,
+ 0.025010156959690527,
+ 0.02476005539009362,
+ 0.024512454836192684,
+ 0.024267330287830756,
+ 0.024024656984952448,
+ 0.023784410415102923,
+ 0.023546566310951898,
+ 0.023311100647842375,
+ 0.02307798964136395,
+ 0.022847209744950314,
+ 0.02261873764750081,
+ 0.022392550271025803,
+ 0.022168624768315544,
+ 0.02194693852063239,
+ 0.021727469135426065,
+ 0.021510194444071803,
+ 0.021295092499631085,
+ 0.021082141574634772,
+ 0.020871320158888425,
+ 0.020662606957299545,
+ 0.020455980887726547,
+ 0.02025142107884928,
+ 0.020048906868060788,
+ 0.01984841779938018,
+ 0.019649933621386374,
+ 0.019453434285172513,
+ 0.019258899942320787,
+ 0.01906631094289758,
+ 0.0188756478334686,
+ 0.018686891355133916,
+ 0.018500022441582577,
+ 0.01831502221716675,
+ 0.018131871994995084,
+ 0.017950553275045134,
+ 0.017771047742294682,
+ 0.017593337264871736,
+ 0.01741740389222302,
+ 0.01724322985330079,
+ 0.017070797554767782,
+ 0.016900089579220106,
+ 0.016731088683427906,
+ 0.016563777796593626,
+ 0.016398140018627688,
+ 0.01623415861844141,
+ 0.016071817032256998,
+ 0.01591109886193443,
+ 0.015751987873315085,
+ 0.015594467994581937,
+ 0.015438523314636117,
+ 0.015284138081489752,
+ 0.015131296700674856,
+ 0.014979983733668108,
+ 0.014830183896331426,
+ 0.014681882057368112,
+ 0.01453506323679443,
+ 0.014389712604426484,
+ 0.01424581547838222,
+ 0.014103357323598397,
+ 0.013962323750362412,
+ 0.013822700512858789,
+ 0.0136844735077302,
+ 0.0135476287726529,
+ 0.01341215248492637,
+ 0.013278030960077106,
+ 0.013145250650476337,
+ 0.01301379814397157,
+ 0.012883660162531854,
+ 0.012754823560906537,
+ 0.01262727532529747,
+ 0.012501002572044496,
+ 0.01237599254632405,
+ 0.01225223262086081,
+ 0.012129710294652202,
+ 0.01200841319170568,
+ 0.011888329059788623,
+ 0.011769445769190735,
+ 0.01165175131149883,
+ 0.011535233798383842,
+ 0.011419881460400004,
+ 0.011305682645796004,
+ 0.011192625819338045,
+ 0.011080699561144665,
+ 0.010969892565533218,
+ 0.010860193639877886,
+ 0.010751591703479106,
+ 0.010644075786444315,
+ 0.010537635028579871,
+ 0.010432258678294072,
+ 0.010327936091511131,
+ 0.010224656730596022,
+ 0.01012241016329006,
+ 0.01002118606165716,
+ 0.009920974201040588,
+ 0.009821764459030182,
+ 0.00972354681443988,
+ 0.00962631134629548,
+ 0.009530048232832523,
+ 0.0094347477505042,
+ 0.009340400272999157,
+ 0.009246996270269163,
+ 0.009154526307566474,
+ 0.009062981044490808,
+ 0.0089723512340459,
+ 0.00888262772170544,
+ 0.008793801444488386,
+ 0.008705863430043502,
+ 0.008618804795743068,
+ 0.008532616747785637,
+ 0.00844729058030778,
+ 0.008362817674504702,
+ 0.008279189497759654,
+ 0.008196397602782058,
+ 0.008114433626754238,
+ 0.008033289290486696,
+ 0.00795295639758183,
+ 0.00787342683360601,
+ 0.007794692565269949,
+ 0.00771674563961725,
+ 0.007639578183221077,
+ 0.007563182401388867,
+ 0.007487550577374978,
+ 0.007412675071601228,
+ 0.007338548320885215,
+ 0.0072651628376763635,
+ 0.0071925112092996,
+ 0.007120586097206604,
+ 0.007049380236234538,
+ 0.006978886433872193,
+ 0.006909097569533471,
+ 0.006840006593838137,
+ 0.0067716065278997555,
+ 0.006703890462620758,
+ 0.006636851557994551
+ ],
+ "yaxis": "y2"
+ },
+ {
+ "mode": "lines",
+ "name": "Score",
+ "type": "scatter",
+ "x": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 60,
+ 61,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 81,
+ 82,
+ 83,
+ 84,
+ 85,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 91,
+ 92,
+ 93,
+ 94,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 108,
+ 109,
+ 110,
+ 111,
+ 112,
+ 113,
+ 114,
+ 115,
+ 116,
+ 117,
+ 118,
+ 119,
+ 120,
+ 121,
+ 122,
+ 123,
+ 124,
+ 125,
+ 126,
+ 127,
+ 128,
+ 129,
+ 130,
+ 131,
+ 132,
+ 133,
+ 134,
+ 135,
+ 136,
+ 137,
+ 138,
+ 139,
+ 140,
+ 141,
+ 142,
+ 143,
+ 144,
+ 145,
+ 146,
+ 147,
+ 148,
+ 149,
+ 150,
+ 151,
+ 152,
+ 153,
+ 154,
+ 155,
+ 156,
+ 157,
+ 158,
+ 159,
+ 160,
+ 161,
+ 162,
+ 163,
+ 164,
+ 165,
+ 166,
+ 167,
+ 168,
+ 169,
+ 170,
+ 171,
+ 172,
+ 173,
+ 174,
+ 175,
+ 176,
+ 177,
+ 178,
+ 179,
+ 180,
+ 181,
+ 182,
+ 183,
+ 184,
+ 185,
+ 186,
+ 187,
+ 188,
+ 189,
+ 190,
+ 191,
+ 192,
+ 193,
+ 194,
+ 195,
+ 196,
+ 197,
+ 198,
+ 199,
+ 200,
+ 201,
+ 202,
+ 203,
+ 204,
+ 205,
+ 206,
+ 207,
+ 208,
+ 209,
+ 210,
+ 211,
+ 212,
+ 213,
+ 214,
+ 215,
+ 216,
+ 217,
+ 218,
+ 219,
+ 220,
+ 221,
+ 222,
+ 223,
+ 224,
+ 225,
+ 226,
+ 227,
+ 228,
+ 229,
+ 230,
+ 231,
+ 232,
+ 233,
+ 234,
+ 235,
+ 236,
+ 237,
+ 238,
+ 239,
+ 240,
+ 241,
+ 242,
+ 243,
+ 244,
+ 245,
+ 246,
+ 247,
+ 248,
+ 249,
+ 250,
+ 251,
+ 252,
+ 253,
+ 254,
+ 255,
+ 256,
+ 257,
+ 258,
+ 259,
+ 260,
+ 261,
+ 262,
+ 263,
+ 264,
+ 265,
+ 266,
+ 267,
+ 268,
+ 269,
+ 270,
+ 271,
+ 272,
+ 273,
+ 274,
+ 275,
+ 276,
+ 277,
+ 278,
+ 279,
+ 280,
+ 281,
+ 282,
+ 283,
+ 284,
+ 285,
+ 286,
+ 287,
+ 288,
+ 289,
+ 290,
+ 291,
+ 292,
+ 293,
+ 294,
+ 295,
+ 296,
+ 297,
+ 298,
+ 299,
+ 300,
+ 301,
+ 302,
+ 303,
+ 304,
+ 305,
+ 306,
+ 307,
+ 308,
+ 309,
+ 310,
+ 311,
+ 312,
+ 313,
+ 314,
+ 315,
+ 316,
+ 317,
+ 318,
+ 319,
+ 320,
+ 321,
+ 322,
+ 323,
+ 324,
+ 325,
+ 326,
+ 327,
+ 328,
+ 329,
+ 330,
+ 331,
+ 332,
+ 333,
+ 334,
+ 335,
+ 336,
+ 337,
+ 338,
+ 339,
+ 340,
+ 341,
+ 342,
+ 343,
+ 344,
+ 345,
+ 346,
+ 347,
+ 348,
+ 349,
+ 350,
+ 351,
+ 352,
+ 353,
+ 354,
+ 355,
+ 356,
+ 357,
+ 358,
+ 359,
+ 360,
+ 361,
+ 362,
+ 363,
+ 364,
+ 365,
+ 366,
+ 367,
+ 368,
+ 369,
+ 370,
+ 371,
+ 372,
+ 373,
+ 374,
+ 375,
+ 376,
+ 377,
+ 378,
+ 379,
+ 380,
+ 381,
+ 382,
+ 383,
+ 384,
+ 385,
+ 386,
+ 387,
+ 388,
+ 389,
+ 390,
+ 391,
+ 392,
+ 393,
+ 394,
+ 395,
+ 396,
+ 397,
+ 398,
+ 399,
+ 400,
+ 401,
+ 402,
+ 403,
+ 404,
+ 405,
+ 406,
+ 407,
+ 408,
+ 409,
+ 410,
+ 411,
+ 412,
+ 413,
+ 414,
+ 415,
+ 416,
+ 417,
+ 418,
+ 419,
+ 420,
+ 421,
+ 422,
+ 423,
+ 424,
+ 425,
+ 426,
+ 427,
+ 428,
+ 429,
+ 430,
+ 431,
+ 432,
+ 433,
+ 434,
+ 435,
+ 436,
+ 437,
+ 438,
+ 439,
+ 440,
+ 441,
+ 442,
+ 443,
+ 444,
+ 445,
+ 446,
+ 447,
+ 448,
+ 449,
+ 450,
+ 451,
+ 452,
+ 453,
+ 454,
+ 455,
+ 456,
+ 457,
+ 458,
+ 459,
+ 460,
+ 461,
+ 462,
+ 463,
+ 464,
+ 465,
+ 466,
+ 467,
+ 468,
+ 469,
+ 470,
+ 471,
+ 472,
+ 473,
+ 474,
+ 475,
+ 476,
+ 477,
+ 478,
+ 479,
+ 480,
+ 481,
+ 482,
+ 483,
+ 484,
+ 485,
+ 486,
+ 487,
+ 488,
+ 489,
+ 490,
+ 491,
+ 492,
+ 493,
+ 494,
+ 495,
+ 496,
+ 497,
+ 498,
+ 499
+ ],
+ "xaxis": "x",
+ "y": [
+ 11,
+ 15,
+ 49,
+ 17,
+ 22,
+ 17,
+ 20,
+ 30,
+ 37,
+ 21,
+ 32,
+ 13,
+ 23,
+ 16,
+ 29,
+ 20,
+ 11,
+ 20,
+ 11,
+ 15,
+ 37,
+ 42,
+ 18,
+ 17,
+ 17,
+ 16,
+ 14,
+ 34,
+ 24,
+ 23,
+ 36,
+ 16,
+ 48,
+ 16,
+ 13,
+ 12,
+ 17,
+ 30,
+ 12,
+ 31,
+ 32,
+ 93,
+ 37,
+ 23,
+ 23,
+ 98,
+ 22,
+ 23,
+ 24,
+ 80,
+ 95,
+ 49,
+ 16,
+ 67,
+ 58,
+ 42,
+ 32,
+ 41,
+ 48,
+ 58,
+ 77,
+ 44,
+ 64,
+ 20,
+ 100,
+ 66,
+ 100,
+ 64,
+ 100,
+ 30,
+ 22,
+ 64,
+ 32,
+ 40,
+ 35,
+ 69,
+ 63,
+ 35,
+ 100,
+ 100,
+ 40,
+ 41,
+ 76,
+ 100,
+ 50,
+ 100,
+ 52,
+ 46,
+ 90,
+ 100,
+ 100,
+ 100,
+ 60,
+ 100,
+ 57,
+ 26,
+ 100,
+ 94,
+ 94,
+ 55,
+ 55,
+ 48,
+ 55,
+ 41,
+ 47,
+ 52,
+ 70,
+ 100,
+ 100,
+ 95,
+ 76,
+ 100,
+ 33,
+ 76,
+ 48,
+ 81,
+ 100,
+ 100,
+ 34,
+ 31,
+ 40,
+ 56,
+ 55,
+ 100,
+ 84,
+ 100,
+ 74,
+ 97,
+ 42,
+ 60,
+ 37,
+ 61,
+ 70,
+ 63,
+ 69,
+ 44,
+ 100,
+ 90,
+ 100,
+ 54,
+ 100,
+ 44,
+ 37,
+ 91,
+ 75,
+ 36,
+ 100,
+ 70,
+ 64,
+ 100,
+ 54,
+ 100,
+ 62,
+ 67,
+ 42,
+ 100,
+ 83,
+ 61,
+ 59,
+ 100,
+ 100,
+ 43,
+ 65,
+ 100,
+ 84,
+ 95,
+ 100,
+ 67,
+ 78,
+ 77,
+ 100,
+ 100,
+ 74,
+ 100,
+ 81,
+ 72,
+ 49,
+ 100,
+ 75,
+ 95,
+ 72,
+ 100,
+ 100,
+ 64,
+ 58,
+ 100,
+ 100,
+ 100,
+ 97,
+ 100,
+ 74,
+ 99,
+ 44,
+ 92,
+ 84,
+ 53,
+ 54,
+ 100,
+ 68,
+ 51,
+ 100,
+ 100,
+ 83,
+ 100,
+ 100,
+ 71,
+ 100,
+ 100,
+ 100,
+ 51,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 85,
+ 82,
+ 56,
+ 100,
+ 100,
+ 89,
+ 100,
+ 90,
+ 88,
+ 100,
+ 95,
+ 100,
+ 100,
+ 100,
+ 100,
+ 91,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 27,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 99,
+ 100,
+ 100,
+ 96,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 73,
+ 100,
+ 100,
+ 100,
+ 98,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 98,
+ 100,
+ 100,
+ 100,
+ 75,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 71,
+ 76,
+ 100,
+ 100,
+ 100,
+ 44,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 89,
+ 100,
+ 100,
+ 80,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 60,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 63,
+ 100,
+ 100,
+ 27,
+ 28,
+ 100,
+ 97,
+ 100,
+ 78,
+ 44,
+ 38,
+ 81,
+ 35,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 65,
+ 100,
+ 80,
+ 88,
+ 100,
+ 100,
+ 99,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 47,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 96,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 31,
+ 100,
+ 100,
+ 97,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 58,
+ 100,
+ 85,
+ 100,
+ 100,
+ 85,
+ 14,
+ 74,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 92,
+ 100,
+ 100,
+ 14,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 66,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 97,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 90,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 93,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 62,
+ 100,
+ 69,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 57,
+ 100,
+ 100,
+ 100
+ ],
+ "yaxis": "y"
+ }
+ ],
+ "layout": {
+ "template": {
+ "data": {
+ "bar": [
+ {
+ "error_x": {
+ "color": "#2a3f5f"
+ },
+ "error_y": {
+ "color": "#2a3f5f"
+ },
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "bar"
+ }
+ ],
+ "barpolar": [
+ {
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "barpolar"
+ }
+ ],
+ "carpet": [
+ {
+ "aaxis": {
+ "endlinecolor": "#2a3f5f",
+ "gridcolor": "white",
+ "linecolor": "white",
+ "minorgridcolor": "white",
+ "startlinecolor": "#2a3f5f"
+ },
+ "baxis": {
+ "endlinecolor": "#2a3f5f",
+ "gridcolor": "white",
+ "linecolor": "white",
+ "minorgridcolor": "white",
+ "startlinecolor": "#2a3f5f"
+ },
+ "type": "carpet"
+ }
+ ],
+ "choropleth": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "choropleth"
+ }
+ ],
+ "contour": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "contour"
+ }
+ ],
+ "contourcarpet": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "contourcarpet"
+ }
+ ],
+ "heatmap": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "heatmap"
+ }
+ ],
+ "heatmapgl": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "heatmapgl"
+ }
+ ],
+ "histogram": [
+ {
+ "marker": {
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "histogram"
+ }
+ ],
+ "histogram2d": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "histogram2d"
+ }
+ ],
+ "histogram2dcontour": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "histogram2dcontour"
+ }
+ ],
+ "mesh3d": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "mesh3d"
+ }
+ ],
+ "parcoords": [
+ {
+ "line": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "parcoords"
+ }
+ ],
+ "pie": [
+ {
+ "automargin": true,
+ "type": "pie"
+ }
+ ],
+ "scatter": [
+ {
+ "fillpattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ },
+ "type": "scatter"
+ }
+ ],
+ "scatter3d": [
+ {
+ "line": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatter3d"
+ }
+ ],
+ "scattercarpet": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattercarpet"
+ }
+ ],
+ "scattergeo": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattergeo"
+ }
+ ],
+ "scattergl": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattergl"
+ }
+ ],
+ "scattermapbox": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattermapbox"
+ }
+ ],
+ "scatterpolar": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterpolar"
+ }
+ ],
+ "scatterpolargl": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterpolargl"
+ }
+ ],
+ "scatterternary": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterternary"
+ }
+ ],
+ "surface": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "surface"
+ }
+ ],
+ "table": [
+ {
+ "cells": {
+ "fill": {
+ "color": "#EBF0F8"
+ },
+ "line": {
+ "color": "white"
+ }
+ },
+ "header": {
+ "fill": {
+ "color": "#C8D4E3"
+ },
+ "line": {
+ "color": "white"
+ }
+ },
+ "type": "table"
+ }
+ ]
+ },
+ "layout": {
+ "annotationdefaults": {
+ "arrowcolor": "#2a3f5f",
+ "arrowhead": 0,
+ "arrowwidth": 1
+ },
+ "autotypenumbers": "strict",
+ "coloraxis": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "colorscale": {
+ "diverging": [
+ [
+ 0,
+ "#8e0152"
+ ],
+ [
+ 0.1,
+ "#c51b7d"
+ ],
+ [
+ 0.2,
+ "#de77ae"
+ ],
+ [
+ 0.3,
+ "#f1b6da"
+ ],
+ [
+ 0.4,
+ "#fde0ef"
+ ],
+ [
+ 0.5,
+ "#f7f7f7"
+ ],
+ [
+ 0.6,
+ "#e6f5d0"
+ ],
+ [
+ 0.7,
+ "#b8e186"
+ ],
+ [
+ 0.8,
+ "#7fbc41"
+ ],
+ [
+ 0.9,
+ "#4d9221"
+ ],
+ [
+ 1,
+ "#276419"
+ ]
+ ],
+ "sequential": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "sequentialminus": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ]
+ },
+ "colorway": [
+ "#636efa",
+ "#EF553B",
+ "#00cc96",
+ "#ab63fa",
+ "#FFA15A",
+ "#19d3f3",
+ "#FF6692",
+ "#B6E880",
+ "#FF97FF",
+ "#FECB52"
+ ],
+ "font": {
+ "color": "#2a3f5f"
+ },
+ "geo": {
+ "bgcolor": "white",
+ "lakecolor": "white",
+ "landcolor": "#E5ECF6",
+ "showlakes": true,
+ "showland": true,
+ "subunitcolor": "white"
+ },
+ "hoverlabel": {
+ "align": "left"
+ },
+ "hovermode": "closest",
+ "mapbox": {
+ "style": "light"
+ },
+ "paper_bgcolor": "white",
+ "plot_bgcolor": "#E5ECF6",
+ "polar": {
+ "angularaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "radialaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "scene": {
+ "xaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "yaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "zaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "caxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "DDQN Training, Advantage Type: AdvantageType.AVG"
+ },
+ "xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 0.94
+ ],
+ "title": {
+ "text": "Episode"
+ }
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Score"
+ }
+ },
+ "yaxis2": {
+ "anchor": "x",
+ "overlaying": "y",
+ "side": "right",
+ "title": {
+ "text": "Exploration Rate"
+ }
+ }
+ }
+ },
+ "image/png": "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",
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a7e47785c2d648c3be9e31fbcc91d6b6",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/500 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "mode": "lines",
+ "name": "Exploration Rate",
+ "type": "scatter",
+ "x": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 60,
+ 61,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 81,
+ 82,
+ 83,
+ 84,
+ 85,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 91,
+ 92,
+ 93,
+ 94,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 108,
+ 109,
+ 110,
+ 111,
+ 112,
+ 113,
+ 114,
+ 115,
+ 116,
+ 117,
+ 118,
+ 119,
+ 120,
+ 121,
+ 122,
+ 123,
+ 124,
+ 125,
+ 126,
+ 127,
+ 128,
+ 129,
+ 130,
+ 131,
+ 132,
+ 133,
+ 134,
+ 135,
+ 136,
+ 137,
+ 138,
+ 139,
+ 140,
+ 141,
+ 142,
+ 143,
+ 144,
+ 145,
+ 146,
+ 147,
+ 148,
+ 149,
+ 150,
+ 151,
+ 152,
+ 153,
+ 154,
+ 155,
+ 156,
+ 157,
+ 158,
+ 159,
+ 160,
+ 161,
+ 162,
+ 163,
+ 164,
+ 165,
+ 166,
+ 167,
+ 168,
+ 169,
+ 170,
+ 171,
+ 172,
+ 173,
+ 174,
+ 175,
+ 176,
+ 177,
+ 178,
+ 179,
+ 180,
+ 181,
+ 182,
+ 183,
+ 184,
+ 185,
+ 186,
+ 187,
+ 188,
+ 189,
+ 190,
+ 191,
+ 192,
+ 193,
+ 194,
+ 195,
+ 196,
+ 197,
+ 198,
+ 199,
+ 200,
+ 201,
+ 202,
+ 203,
+ 204,
+ 205,
+ 206,
+ 207,
+ 208,
+ 209,
+ 210,
+ 211,
+ 212,
+ 213,
+ 214,
+ 215,
+ 216,
+ 217,
+ 218,
+ 219,
+ 220,
+ 221,
+ 222,
+ 223,
+ 224,
+ 225,
+ 226,
+ 227,
+ 228,
+ 229,
+ 230,
+ 231,
+ 232,
+ 233,
+ 234,
+ 235,
+ 236,
+ 237,
+ 238,
+ 239,
+ 240,
+ 241,
+ 242,
+ 243,
+ 244,
+ 245,
+ 246,
+ 247,
+ 248,
+ 249,
+ 250,
+ 251,
+ 252,
+ 253,
+ 254,
+ 255,
+ 256,
+ 257,
+ 258,
+ 259,
+ 260,
+ 261,
+ 262,
+ 263,
+ 264,
+ 265,
+ 266,
+ 267,
+ 268,
+ 269,
+ 270,
+ 271,
+ 272,
+ 273,
+ 274,
+ 275,
+ 276,
+ 277,
+ 278,
+ 279,
+ 280,
+ 281,
+ 282,
+ 283,
+ 284,
+ 285,
+ 286,
+ 287,
+ 288,
+ 289,
+ 290,
+ 291,
+ 292,
+ 293,
+ 294,
+ 295,
+ 296,
+ 297,
+ 298,
+ 299,
+ 300,
+ 301,
+ 302,
+ 303,
+ 304,
+ 305,
+ 306,
+ 307,
+ 308,
+ 309,
+ 310,
+ 311,
+ 312,
+ 313,
+ 314,
+ 315,
+ 316,
+ 317,
+ 318,
+ 319,
+ 320,
+ 321,
+ 322,
+ 323,
+ 324,
+ 325,
+ 326,
+ 327,
+ 328,
+ 329,
+ 330,
+ 331,
+ 332,
+ 333,
+ 334,
+ 335,
+ 336,
+ 337,
+ 338,
+ 339,
+ 340,
+ 341,
+ 342,
+ 343,
+ 344,
+ 345,
+ 346,
+ 347,
+ 348,
+ 349,
+ 350,
+ 351,
+ 352,
+ 353,
+ 354,
+ 355,
+ 356,
+ 357,
+ 358,
+ 359,
+ 360,
+ 361,
+ 362,
+ 363,
+ 364,
+ 365,
+ 366,
+ 367,
+ 368,
+ 369,
+ 370,
+ 371,
+ 372,
+ 373,
+ 374,
+ 375,
+ 376,
+ 377,
+ 378,
+ 379,
+ 380,
+ 381,
+ 382,
+ 383,
+ 384,
+ 385,
+ 386,
+ 387,
+ 388,
+ 389,
+ 390,
+ 391,
+ 392,
+ 393,
+ 394,
+ 395,
+ 396,
+ 397,
+ 398,
+ 399,
+ 400,
+ 401,
+ 402,
+ 403,
+ 404,
+ 405,
+ 406,
+ 407,
+ 408,
+ 409,
+ 410,
+ 411,
+ 412,
+ 413,
+ 414,
+ 415,
+ 416,
+ 417,
+ 418,
+ 419,
+ 420,
+ 421,
+ 422,
+ 423,
+ 424,
+ 425,
+ 426,
+ 427,
+ 428,
+ 429,
+ 430,
+ 431,
+ 432,
+ 433,
+ 434,
+ 435,
+ 436,
+ 437,
+ 438,
+ 439,
+ 440,
+ 441,
+ 442,
+ 443,
+ 444,
+ 445,
+ 446,
+ 447,
+ 448,
+ 449,
+ 450,
+ 451,
+ 452,
+ 453,
+ 454,
+ 455,
+ 456,
+ 457,
+ 458,
+ 459,
+ 460,
+ 461,
+ 462,
+ 463,
+ 464,
+ 465,
+ 466,
+ 467,
+ 468,
+ 469,
+ 470,
+ 471,
+ 472,
+ 473,
+ 474,
+ 475,
+ 476,
+ 477,
+ 478,
+ 479,
+ 480,
+ 481,
+ 482,
+ 483,
+ 484,
+ 485,
+ 486,
+ 487,
+ 488,
+ 489,
+ 490,
+ 491,
+ 492,
+ 493,
+ 494,
+ 495,
+ 496,
+ 497,
+ 498,
+ 499
+ ],
+ "xaxis": "x",
+ "y": [
+ 1,
+ 0.99,
+ 0.9801,
+ 0.970299,
+ 0.96059601,
+ 0.9509900499,
+ 0.941480149401,
+ 0.93206534790699,
+ 0.92274469442792,
+ 0.9135172474836408,
+ 0.9043820750088044,
+ 0.8953382542587163,
+ 0.8863848717161291,
+ 0.8775210229989678,
+ 0.8687458127689781,
+ 0.8600583546412883,
+ 0.8514577710948754,
+ 0.8429431933839266,
+ 0.8345137614500874,
+ 0.8261686238355865,
+ 0.8179069375972307,
+ 0.8097278682212583,
+ 0.8016305895390458,
+ 0.7936142836436553,
+ 0.7856781408072188,
+ 0.7778213593991465,
+ 0.7700431458051551,
+ 0.7623427143471035,
+ 0.7547192872036325,
+ 0.7471720943315961,
+ 0.7397003733882802,
+ 0.7323033696543974,
+ 0.7249803359578534,
+ 0.7177305325982748,
+ 0.7105532272722921,
+ 0.7034476949995692,
+ 0.6964132180495735,
+ 0.6894490858690777,
+ 0.682554595010387,
+ 0.6757290490602831,
+ 0.6689717585696803,
+ 0.6622820409839835,
+ 0.6556592205741436,
+ 0.6491026283684022,
+ 0.6426116020847181,
+ 0.6361854860638709,
+ 0.6298236312032323,
+ 0.6235253948912,
+ 0.617290140942288,
+ 0.6111172395328651,
+ 0.6050060671375365,
+ 0.5989560064661611,
+ 0.5929664464014994,
+ 0.5870367819374844,
+ 0.5811664141181095,
+ 0.5753547499769285,
+ 0.5696012024771592,
+ 0.5639051904523876,
+ 0.5582661385478638,
+ 0.5526834771623851,
+ 0.5471566423907612,
+ 0.5416850759668536,
+ 0.536268225207185,
+ 0.5309055429551132,
+ 0.525596487525562,
+ 0.5203405226503064,
+ 0.5151371174238033,
+ 0.5099857462495653,
+ 0.5048858887870696,
+ 0.4998370298991989,
+ 0.49483865960020695,
+ 0.4898902730042049,
+ 0.4849913702741629,
+ 0.4801414565714212,
+ 0.475340042005707,
+ 0.47058664158564995,
+ 0.4658807751697934,
+ 0.4612219674180955,
+ 0.45660974774391455,
+ 0.4520436502664754,
+ 0.44752321376381066,
+ 0.44304798162617254,
+ 0.4386175018099108,
+ 0.4342313267918117,
+ 0.4298890135238936,
+ 0.42559012338865465,
+ 0.4213342221547681,
+ 0.4171208799332205,
+ 0.41294967113388825,
+ 0.40882017442254937,
+ 0.4047319726783239,
+ 0.40068465295154065,
+ 0.39667780642202527,
+ 0.392711028357805,
+ 0.38878391807422696,
+ 0.3848960788934847,
+ 0.38104711810454983,
+ 0.37723664692350434,
+ 0.37346428045426927,
+ 0.36972963764972655,
+ 0.36603234127322926,
+ 0.36237201786049694,
+ 0.358748297681892,
+ 0.35516081470507305,
+ 0.3516092065580223,
+ 0.34809311449244207,
+ 0.34461218334751764,
+ 0.34116606151404244,
+ 0.337754400898902,
+ 0.334376856889913,
+ 0.33103308832101386,
+ 0.3277227574378037,
+ 0.3244455298634257,
+ 0.3212010745647914,
+ 0.3179890638191435,
+ 0.31480917318095203,
+ 0.3116610814491425,
+ 0.30854447063465107,
+ 0.30545902592830454,
+ 0.3024044356690215,
+ 0.29938039131233124,
+ 0.2963865873992079,
+ 0.29342272152521587,
+ 0.2904884943099637,
+ 0.28758360936686406,
+ 0.2847077732731954,
+ 0.28186069554046345,
+ 0.2790420885850588,
+ 0.2762516676992082,
+ 0.27348915102221616,
+ 0.270754259511994,
+ 0.26804671691687404,
+ 0.2653662497477053,
+ 0.2627125872502282,
+ 0.2600854613777259,
+ 0.2574846067639487,
+ 0.2549097606963092,
+ 0.2523606630893461,
+ 0.24983705645845267,
+ 0.24733868589386815,
+ 0.24486529903492943,
+ 0.24241664604458016,
+ 0.23999247958413436,
+ 0.23759255478829305,
+ 0.2352166292404101,
+ 0.232864462948006,
+ 0.23053581831852593,
+ 0.22823046013534068,
+ 0.22594815553398728,
+ 0.22368867397864745,
+ 0.22145178723886097,
+ 0.2192372693664723,
+ 0.2170448966728076,
+ 0.21487444770607952,
+ 0.2127257032290187,
+ 0.21059844619672857,
+ 0.20849246173476127,
+ 0.2064075371174137,
+ 0.20434346174623952,
+ 0.20230002712877712,
+ 0.20027702685748935,
+ 0.19827425658891443,
+ 0.1962915140230253,
+ 0.19432859888279505,
+ 0.1923853128939671,
+ 0.19046145976502743,
+ 0.1885568451673771,
+ 0.18667127671570335,
+ 0.18480456394854633,
+ 0.18295651830906087,
+ 0.18112695312597027,
+ 0.17931568359471056,
+ 0.17752252675876343,
+ 0.17574730149117582,
+ 0.17398982847626407,
+ 0.17224993019150142,
+ 0.1705274308895864,
+ 0.16882215658069055,
+ 0.16713393501488363,
+ 0.1654625956647348,
+ 0.16380796970808745,
+ 0.16216989001100657,
+ 0.1605481911108965,
+ 0.15894270919978754,
+ 0.15735328210778965,
+ 0.15577974928671176,
+ 0.15422195179384465,
+ 0.1526797322759062,
+ 0.15115293495314713,
+ 0.14964140560361566,
+ 0.1481449915475795,
+ 0.1466635416321037,
+ 0.14519690621578268,
+ 0.14374493715362485,
+ 0.1423074877820886,
+ 0.1408844129042677,
+ 0.13947556877522502,
+ 0.13808081308747278,
+ 0.13670000495659804,
+ 0.13533300490703207,
+ 0.13397967485796175,
+ 0.13263987810938213,
+ 0.1313134793282883,
+ 0.13000034453500542,
+ 0.12870034108965536,
+ 0.1274133376787588,
+ 0.12613920430197123,
+ 0.12487781225895152,
+ 0.123629034136362,
+ 0.12239274379499838,
+ 0.1211688163570484,
+ 0.11995712819347792,
+ 0.11875755691154316,
+ 0.11756998134242772,
+ 0.11639428152900344,
+ 0.1152303387137134,
+ 0.11407803532657627,
+ 0.11293725497331052,
+ 0.1118078824235774,
+ 0.11068980359934164,
+ 0.10958290556334822,
+ 0.10848707650771476,
+ 0.1074022057426376,
+ 0.10632818368521124,
+ 0.10526490184835911,
+ 0.10421225282987552,
+ 0.10317013030157676,
+ 0.102138428998561,
+ 0.10111704470857538,
+ 0.10010587426148965,
+ 0.09910481551887472,
+ 0.098113767363686,
+ 0.09713262969004911,
+ 0.09616130339314864,
+ 0.09519969035921716,
+ 0.09424769345562498,
+ 0.09330521652106873,
+ 0.09237216435585804,
+ 0.09144844271229946,
+ 0.09053395828517646,
+ 0.08962861870232469,
+ 0.08873233251530144,
+ 0.08784500919014843,
+ 0.08696655909824694,
+ 0.08609689350726446,
+ 0.08523592457219181,
+ 0.0843835653264699,
+ 0.0835397296732052,
+ 0.08270433237647315,
+ 0.08187728905270841,
+ 0.08105851616218133,
+ 0.08024793100055952,
+ 0.07944545169055392,
+ 0.07865099717364837,
+ 0.07786448720191189,
+ 0.07708584232989277,
+ 0.07631498390659384,
+ 0.07555183406752791,
+ 0.07479631572685264,
+ 0.07404835256958411,
+ 0.07330786904388827,
+ 0.07257479035344938,
+ 0.07184904244991488,
+ 0.07113055202541574,
+ 0.07041924650516158,
+ 0.06971505404010997,
+ 0.06901790349970886,
+ 0.06832772446471178,
+ 0.06764444722006466,
+ 0.066968002747864,
+ 0.06629832272038537,
+ 0.06563533949318151,
+ 0.06497898609824969,
+ 0.0643291962372672,
+ 0.06368590427489453,
+ 0.06304904523214558,
+ 0.06241855477982412,
+ 0.06179436923202588,
+ 0.06117642553970562,
+ 0.06056466128430856,
+ 0.05995901467146548,
+ 0.05935942452475082,
+ 0.058765830279503314,
+ 0.05817817197670828,
+ 0.057596390256941195,
+ 0.05702042635437178,
+ 0.05645022209082806,
+ 0.05588571986991978,
+ 0.055326862671220584,
+ 0.05477359404450838,
+ 0.054225858104063294,
+ 0.05368359952302266,
+ 0.053146763527792434,
+ 0.052615295892514506,
+ 0.05208914293358936,
+ 0.05156825150425347,
+ 0.051052568989210935,
+ 0.05054204329931883,
+ 0.05003662286632564,
+ 0.04953625663766238,
+ 0.04904089407128576,
+ 0.0485504851305729,
+ 0.048064980279267165,
+ 0.04758433047647449,
+ 0.04710848717170975,
+ 0.04663740229999265,
+ 0.04617102827699272,
+ 0.045709317994222794,
+ 0.04525222481428057,
+ 0.04479970256613776,
+ 0.04435170554047638,
+ 0.043908188485071616,
+ 0.0434691066002209,
+ 0.04303441553421869,
+ 0.0426040713788765,
+ 0.04217803066508773,
+ 0.04175625035843686,
+ 0.041338687854852486,
+ 0.04092530097630396,
+ 0.04051604796654091,
+ 0.04011088748687551,
+ 0.03970977861200675,
+ 0.03931268082588668,
+ 0.03891955401762781,
+ 0.03853035847745153,
+ 0.03814505489267701,
+ 0.03776360434375024,
+ 0.03738596830031274,
+ 0.03701210861730961,
+ 0.03664198753113651,
+ 0.036275567655825146,
+ 0.03591281197926689,
+ 0.035553683859474224,
+ 0.03519814702087948,
+ 0.03484616555067068,
+ 0.034497703895163975,
+ 0.03415272685621234,
+ 0.03381119958765021,
+ 0.03347308759177371,
+ 0.03313835671585597,
+ 0.03280697314869741,
+ 0.032478903417210436,
+ 0.032154114383038335,
+ 0.03183257323920795,
+ 0.03151424750681587,
+ 0.03119910503174771,
+ 0.030887113981430233,
+ 0.03057824284161593,
+ 0.03027246041319977,
+ 0.029969735809067772,
+ 0.029670038450977095,
+ 0.029373338066467324,
+ 0.02907960468580265,
+ 0.028788808638944625,
+ 0.028500920552555178,
+ 0.028215911347029624,
+ 0.027933752233559327,
+ 0.027654414711223735,
+ 0.027377870564111496,
+ 0.027104091858470385,
+ 0.026833050939885677,
+ 0.02656472043048682,
+ 0.02629907322618195,
+ 0.026036082493920133,
+ 0.02577572166898093,
+ 0.02551796445229112,
+ 0.02526278480776821,
+ 0.025010156959690527,
+ 0.02476005539009362,
+ 0.024512454836192684,
+ 0.024267330287830756,
+ 0.024024656984952448,
+ 0.023784410415102923,
+ 0.023546566310951898,
+ 0.023311100647842375,
+ 0.02307798964136395,
+ 0.022847209744950314,
+ 0.02261873764750081,
+ 0.022392550271025803,
+ 0.022168624768315544,
+ 0.02194693852063239,
+ 0.021727469135426065,
+ 0.021510194444071803,
+ 0.021295092499631085,
+ 0.021082141574634772,
+ 0.020871320158888425,
+ 0.020662606957299545,
+ 0.020455980887726547,
+ 0.02025142107884928,
+ 0.020048906868060788,
+ 0.01984841779938018,
+ 0.019649933621386374,
+ 0.019453434285172513,
+ 0.019258899942320787,
+ 0.01906631094289758,
+ 0.0188756478334686,
+ 0.018686891355133916,
+ 0.018500022441582577,
+ 0.01831502221716675,
+ 0.018131871994995084,
+ 0.017950553275045134,
+ 0.017771047742294682,
+ 0.017593337264871736,
+ 0.01741740389222302,
+ 0.01724322985330079,
+ 0.017070797554767782,
+ 0.016900089579220106,
+ 0.016731088683427906,
+ 0.016563777796593626,
+ 0.016398140018627688,
+ 0.01623415861844141,
+ 0.016071817032256998,
+ 0.01591109886193443,
+ 0.015751987873315085,
+ 0.015594467994581937,
+ 0.015438523314636117,
+ 0.015284138081489752,
+ 0.015131296700674856,
+ 0.014979983733668108,
+ 0.014830183896331426,
+ 0.014681882057368112,
+ 0.01453506323679443,
+ 0.014389712604426484,
+ 0.01424581547838222,
+ 0.014103357323598397,
+ 0.013962323750362412,
+ 0.013822700512858789,
+ 0.0136844735077302,
+ 0.0135476287726529,
+ 0.01341215248492637,
+ 0.013278030960077106,
+ 0.013145250650476337,
+ 0.01301379814397157,
+ 0.012883660162531854,
+ 0.012754823560906537,
+ 0.01262727532529747,
+ 0.012501002572044496,
+ 0.01237599254632405,
+ 0.01225223262086081,
+ 0.012129710294652202,
+ 0.01200841319170568,
+ 0.011888329059788623,
+ 0.011769445769190735,
+ 0.01165175131149883,
+ 0.011535233798383842,
+ 0.011419881460400004,
+ 0.011305682645796004,
+ 0.011192625819338045,
+ 0.011080699561144665,
+ 0.010969892565533218,
+ 0.010860193639877886,
+ 0.010751591703479106,
+ 0.010644075786444315,
+ 0.010537635028579871,
+ 0.010432258678294072,
+ 0.010327936091511131,
+ 0.010224656730596022,
+ 0.01012241016329006,
+ 0.01002118606165716,
+ 0.009920974201040588,
+ 0.009821764459030182,
+ 0.00972354681443988,
+ 0.00962631134629548,
+ 0.009530048232832523,
+ 0.0094347477505042,
+ 0.009340400272999157,
+ 0.009246996270269163,
+ 0.009154526307566474,
+ 0.009062981044490808,
+ 0.0089723512340459,
+ 0.00888262772170544,
+ 0.008793801444488386,
+ 0.008705863430043502,
+ 0.008618804795743068,
+ 0.008532616747785637,
+ 0.00844729058030778,
+ 0.008362817674504702,
+ 0.008279189497759654,
+ 0.008196397602782058,
+ 0.008114433626754238,
+ 0.008033289290486696,
+ 0.00795295639758183,
+ 0.00787342683360601,
+ 0.007794692565269949,
+ 0.00771674563961725,
+ 0.007639578183221077,
+ 0.007563182401388867,
+ 0.007487550577374978,
+ 0.007412675071601228,
+ 0.007338548320885215,
+ 0.0072651628376763635,
+ 0.0071925112092996,
+ 0.007120586097206604,
+ 0.007049380236234538,
+ 0.006978886433872193,
+ 0.006909097569533471,
+ 0.006840006593838137,
+ 0.0067716065278997555,
+ 0.006703890462620758,
+ 0.006636851557994551
+ ],
+ "yaxis": "y2"
+ },
+ {
+ "mode": "lines",
+ "name": "Score",
+ "type": "scatter",
+ "x": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 60,
+ 61,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 81,
+ 82,
+ 83,
+ 84,
+ 85,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 91,
+ 92,
+ 93,
+ 94,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 108,
+ 109,
+ 110,
+ 111,
+ 112,
+ 113,
+ 114,
+ 115,
+ 116,
+ 117,
+ 118,
+ 119,
+ 120,
+ 121,
+ 122,
+ 123,
+ 124,
+ 125,
+ 126,
+ 127,
+ 128,
+ 129,
+ 130,
+ 131,
+ 132,
+ 133,
+ 134,
+ 135,
+ 136,
+ 137,
+ 138,
+ 139,
+ 140,
+ 141,
+ 142,
+ 143,
+ 144,
+ 145,
+ 146,
+ 147,
+ 148,
+ 149,
+ 150,
+ 151,
+ 152,
+ 153,
+ 154,
+ 155,
+ 156,
+ 157,
+ 158,
+ 159,
+ 160,
+ 161,
+ 162,
+ 163,
+ 164,
+ 165,
+ 166,
+ 167,
+ 168,
+ 169,
+ 170,
+ 171,
+ 172,
+ 173,
+ 174,
+ 175,
+ 176,
+ 177,
+ 178,
+ 179,
+ 180,
+ 181,
+ 182,
+ 183,
+ 184,
+ 185,
+ 186,
+ 187,
+ 188,
+ 189,
+ 190,
+ 191,
+ 192,
+ 193,
+ 194,
+ 195,
+ 196,
+ 197,
+ 198,
+ 199,
+ 200,
+ 201,
+ 202,
+ 203,
+ 204,
+ 205,
+ 206,
+ 207,
+ 208,
+ 209,
+ 210,
+ 211,
+ 212,
+ 213,
+ 214,
+ 215,
+ 216,
+ 217,
+ 218,
+ 219,
+ 220,
+ 221,
+ 222,
+ 223,
+ 224,
+ 225,
+ 226,
+ 227,
+ 228,
+ 229,
+ 230,
+ 231,
+ 232,
+ 233,
+ 234,
+ 235,
+ 236,
+ 237,
+ 238,
+ 239,
+ 240,
+ 241,
+ 242,
+ 243,
+ 244,
+ 245,
+ 246,
+ 247,
+ 248,
+ 249,
+ 250,
+ 251,
+ 252,
+ 253,
+ 254,
+ 255,
+ 256,
+ 257,
+ 258,
+ 259,
+ 260,
+ 261,
+ 262,
+ 263,
+ 264,
+ 265,
+ 266,
+ 267,
+ 268,
+ 269,
+ 270,
+ 271,
+ 272,
+ 273,
+ 274,
+ 275,
+ 276,
+ 277,
+ 278,
+ 279,
+ 280,
+ 281,
+ 282,
+ 283,
+ 284,
+ 285,
+ 286,
+ 287,
+ 288,
+ 289,
+ 290,
+ 291,
+ 292,
+ 293,
+ 294,
+ 295,
+ 296,
+ 297,
+ 298,
+ 299,
+ 300,
+ 301,
+ 302,
+ 303,
+ 304,
+ 305,
+ 306,
+ 307,
+ 308,
+ 309,
+ 310,
+ 311,
+ 312,
+ 313,
+ 314,
+ 315,
+ 316,
+ 317,
+ 318,
+ 319,
+ 320,
+ 321,
+ 322,
+ 323,
+ 324,
+ 325,
+ 326,
+ 327,
+ 328,
+ 329,
+ 330,
+ 331,
+ 332,
+ 333,
+ 334,
+ 335,
+ 336,
+ 337,
+ 338,
+ 339,
+ 340,
+ 341,
+ 342,
+ 343,
+ 344,
+ 345,
+ 346,
+ 347,
+ 348,
+ 349,
+ 350,
+ 351,
+ 352,
+ 353,
+ 354,
+ 355,
+ 356,
+ 357,
+ 358,
+ 359,
+ 360,
+ 361,
+ 362,
+ 363,
+ 364,
+ 365,
+ 366,
+ 367,
+ 368,
+ 369,
+ 370,
+ 371,
+ 372,
+ 373,
+ 374,
+ 375,
+ 376,
+ 377,
+ 378,
+ 379,
+ 380,
+ 381,
+ 382,
+ 383,
+ 384,
+ 385,
+ 386,
+ 387,
+ 388,
+ 389,
+ 390,
+ 391,
+ 392,
+ 393,
+ 394,
+ 395,
+ 396,
+ 397,
+ 398,
+ 399,
+ 400,
+ 401,
+ 402,
+ 403,
+ 404,
+ 405,
+ 406,
+ 407,
+ 408,
+ 409,
+ 410,
+ 411,
+ 412,
+ 413,
+ 414,
+ 415,
+ 416,
+ 417,
+ 418,
+ 419,
+ 420,
+ 421,
+ 422,
+ 423,
+ 424,
+ 425,
+ 426,
+ 427,
+ 428,
+ 429,
+ 430,
+ 431,
+ 432,
+ 433,
+ 434,
+ 435,
+ 436,
+ 437,
+ 438,
+ 439,
+ 440,
+ 441,
+ 442,
+ 443,
+ 444,
+ 445,
+ 446,
+ 447,
+ 448,
+ 449,
+ 450,
+ 451,
+ 452,
+ 453,
+ 454,
+ 455,
+ 456,
+ 457,
+ 458,
+ 459,
+ 460,
+ 461,
+ 462,
+ 463,
+ 464,
+ 465,
+ 466,
+ 467,
+ 468,
+ 469,
+ 470,
+ 471,
+ 472,
+ 473,
+ 474,
+ 475,
+ 476,
+ 477,
+ 478,
+ 479,
+ 480,
+ 481,
+ 482,
+ 483,
+ 484,
+ 485,
+ 486,
+ 487,
+ 488,
+ 489,
+ 490,
+ 491,
+ 492,
+ 493,
+ 494,
+ 495,
+ 496,
+ 497,
+ 498,
+ 499
+ ],
+ "xaxis": "x",
+ "y": [
+ 45,
+ 15,
+ 19,
+ 16,
+ 13,
+ 30,
+ 27,
+ 10,
+ 15,
+ 34,
+ 32,
+ 28,
+ 20,
+ 25,
+ 28,
+ 25,
+ 20,
+ 10,
+ 43,
+ 36,
+ 18,
+ 11,
+ 15,
+ 36,
+ 33,
+ 35,
+ 13,
+ 15,
+ 24,
+ 63,
+ 18,
+ 12,
+ 35,
+ 16,
+ 24,
+ 20,
+ 50,
+ 19,
+ 31,
+ 16,
+ 14,
+ 32,
+ 30,
+ 48,
+ 20,
+ 68,
+ 17,
+ 28,
+ 19,
+ 16,
+ 35,
+ 53,
+ 40,
+ 99,
+ 48,
+ 49,
+ 35,
+ 38,
+ 59,
+ 46,
+ 57,
+ 63,
+ 16,
+ 99,
+ 29,
+ 66,
+ 47,
+ 61,
+ 52,
+ 46,
+ 41,
+ 54,
+ 17,
+ 40,
+ 100,
+ 80,
+ 28,
+ 57,
+ 58,
+ 90,
+ 14,
+ 66,
+ 69,
+ 42,
+ 36,
+ 32,
+ 23,
+ 26,
+ 100,
+ 72,
+ 64,
+ 51,
+ 87,
+ 36,
+ 40,
+ 75,
+ 83,
+ 100,
+ 57,
+ 52,
+ 99,
+ 100,
+ 92,
+ 53,
+ 100,
+ 100,
+ 74,
+ 77,
+ 63,
+ 79,
+ 100,
+ 63,
+ 100,
+ 76,
+ 98,
+ 100,
+ 85,
+ 48,
+ 27,
+ 100,
+ 76,
+ 44,
+ 47,
+ 87,
+ 56,
+ 100,
+ 16,
+ 26,
+ 24,
+ 36,
+ 71,
+ 33,
+ 35,
+ 61,
+ 47,
+ 54,
+ 31,
+ 57,
+ 60,
+ 17,
+ 16,
+ 35,
+ 30,
+ 67,
+ 89,
+ 65,
+ 77,
+ 73,
+ 29,
+ 27,
+ 31,
+ 28,
+ 27,
+ 84,
+ 57,
+ 100,
+ 20,
+ 36,
+ 73,
+ 16,
+ 54,
+ 19,
+ 92,
+ 23,
+ 28,
+ 99,
+ 65,
+ 25,
+ 45,
+ 40,
+ 24,
+ 27,
+ 36,
+ 13,
+ 34,
+ 46,
+ 40,
+ 100,
+ 58,
+ 42,
+ 100,
+ 59,
+ 19,
+ 82,
+ 40,
+ 85,
+ 28,
+ 83,
+ 14,
+ 55,
+ 50,
+ 16,
+ 70,
+ 29,
+ 34,
+ 43,
+ 19,
+ 33,
+ 61,
+ 27,
+ 40,
+ 18,
+ 45,
+ 52,
+ 91,
+ 36,
+ 87,
+ 84,
+ 36,
+ 31,
+ 83,
+ 100,
+ 21,
+ 77,
+ 90,
+ 99,
+ 65,
+ 47,
+ 30,
+ 100,
+ 29,
+ 56,
+ 100,
+ 85,
+ 92,
+ 11,
+ 93,
+ 100,
+ 26,
+ 50,
+ 62,
+ 24,
+ 60,
+ 34,
+ 43,
+ 79,
+ 88,
+ 70,
+ 26,
+ 100,
+ 42,
+ 37,
+ 30,
+ 47,
+ 23,
+ 65,
+ 100,
+ 63,
+ 47,
+ 70,
+ 22,
+ 59,
+ 65,
+ 53,
+ 43,
+ 100,
+ 47,
+ 93,
+ 100,
+ 71,
+ 26,
+ 18,
+ 62,
+ 31,
+ 90,
+ 40,
+ 67,
+ 100,
+ 69,
+ 100,
+ 79,
+ 54,
+ 100,
+ 79,
+ 30,
+ 56,
+ 100,
+ 61,
+ 49,
+ 100,
+ 52,
+ 70,
+ 57,
+ 32,
+ 86,
+ 25,
+ 18,
+ 100,
+ 40,
+ 76,
+ 88,
+ 84,
+ 67,
+ 100,
+ 88,
+ 28,
+ 36,
+ 74,
+ 98,
+ 73,
+ 67,
+ 72,
+ 67,
+ 60,
+ 86,
+ 87,
+ 53,
+ 90,
+ 51,
+ 38,
+ 74,
+ 100,
+ 100,
+ 67,
+ 63,
+ 26,
+ 58,
+ 63,
+ 100,
+ 100,
+ 65,
+ 84,
+ 100,
+ 27,
+ 100,
+ 88,
+ 41,
+ 100,
+ 71,
+ 100,
+ 43,
+ 95,
+ 58,
+ 100,
+ 61,
+ 43,
+ 37,
+ 100,
+ 100,
+ 100,
+ 100,
+ 87,
+ 72,
+ 67,
+ 72,
+ 66,
+ 50,
+ 45,
+ 65,
+ 70,
+ 100,
+ 85,
+ 67,
+ 46,
+ 97,
+ 52,
+ 73,
+ 100,
+ 50,
+ 100,
+ 100,
+ 56,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 69,
+ 74,
+ 100,
+ 75,
+ 79,
+ 100,
+ 49,
+ 100,
+ 16,
+ 89,
+ 69,
+ 100,
+ 69,
+ 48,
+ 44,
+ 100,
+ 33,
+ 62,
+ 100,
+ 84,
+ 71,
+ 22,
+ 100,
+ 69,
+ 52,
+ 75,
+ 100,
+ 42,
+ 53,
+ 57,
+ 75,
+ 47,
+ 100,
+ 53,
+ 100,
+ 68,
+ 54,
+ 65,
+ 88,
+ 49,
+ 99,
+ 69,
+ 61,
+ 68,
+ 71,
+ 56,
+ 100,
+ 94,
+ 37,
+ 99,
+ 89,
+ 63,
+ 84,
+ 100,
+ 91,
+ 82,
+ 100,
+ 100,
+ 64,
+ 93,
+ 57,
+ 100,
+ 100,
+ 77,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 59,
+ 100,
+ 67,
+ 70,
+ 94,
+ 100,
+ 91,
+ 100,
+ 100,
+ 46,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 62,
+ 100,
+ 72,
+ 89,
+ 91,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 40,
+ 92,
+ 81,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 100,
+ 74,
+ 100,
+ 82,
+ 100,
+ 74,
+ 100,
+ 66,
+ 100,
+ 100,
+ 100,
+ 100,
+ 63,
+ 100,
+ 100,
+ 87,
+ 100,
+ 87,
+ 66,
+ 100,
+ 100,
+ 100,
+ 100,
+ 70,
+ 100
+ ],
+ "yaxis": "y"
+ }
+ ],
+ "layout": {
+ "template": {
+ "data": {
+ "bar": [
+ {
+ "error_x": {
+ "color": "#2a3f5f"
+ },
+ "error_y": {
+ "color": "#2a3f5f"
+ },
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "bar"
+ }
+ ],
+ "barpolar": [
+ {
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "barpolar"
+ }
+ ],
+ "carpet": [
+ {
+ "aaxis": {
+ "endlinecolor": "#2a3f5f",
+ "gridcolor": "white",
+ "linecolor": "white",
+ "minorgridcolor": "white",
+ "startlinecolor": "#2a3f5f"
+ },
+ "baxis": {
+ "endlinecolor": "#2a3f5f",
+ "gridcolor": "white",
+ "linecolor": "white",
+ "minorgridcolor": "white",
+ "startlinecolor": "#2a3f5f"
+ },
+ "type": "carpet"
+ }
+ ],
+ "choropleth": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "choropleth"
+ }
+ ],
+ "contour": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "contour"
+ }
+ ],
+ "contourcarpet": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "contourcarpet"
+ }
+ ],
+ "heatmap": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "heatmap"
+ }
+ ],
+ "heatmapgl": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "heatmapgl"
+ }
+ ],
+ "histogram": [
+ {
+ "marker": {
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "histogram"
+ }
+ ],
+ "histogram2d": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "histogram2d"
+ }
+ ],
+ "histogram2dcontour": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "histogram2dcontour"
+ }
+ ],
+ "mesh3d": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "type": "mesh3d"
+ }
+ ],
+ "parcoords": [
+ {
+ "line": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "parcoords"
+ }
+ ],
+ "pie": [
+ {
+ "automargin": true,
+ "type": "pie"
+ }
+ ],
+ "scatter": [
+ {
+ "fillpattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ },
+ "type": "scatter"
+ }
+ ],
+ "scatter3d": [
+ {
+ "line": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatter3d"
+ }
+ ],
+ "scattercarpet": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattercarpet"
+ }
+ ],
+ "scattergeo": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattergeo"
+ }
+ ],
+ "scattergl": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattergl"
+ }
+ ],
+ "scattermapbox": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scattermapbox"
+ }
+ ],
+ "scatterpolar": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterpolar"
+ }
+ ],
+ "scatterpolargl": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterpolargl"
+ }
+ ],
+ "scatterternary": [
+ {
+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "type": "scatterternary"
+ }
+ ],
+ "surface": [
+ {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ },
+ "colorscale": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "type": "surface"
+ }
+ ],
+ "table": [
+ {
+ "cells": {
+ "fill": {
+ "color": "#EBF0F8"
+ },
+ "line": {
+ "color": "white"
+ }
+ },
+ "header": {
+ "fill": {
+ "color": "#C8D4E3"
+ },
+ "line": {
+ "color": "white"
+ }
+ },
+ "type": "table"
+ }
+ ]
+ },
+ "layout": {
+ "annotationdefaults": {
+ "arrowcolor": "#2a3f5f",
+ "arrowhead": 0,
+ "arrowwidth": 1
+ },
+ "autotypenumbers": "strict",
+ "coloraxis": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
+ },
+ "colorscale": {
+ "diverging": [
+ [
+ 0,
+ "#8e0152"
+ ],
+ [
+ 0.1,
+ "#c51b7d"
+ ],
+ [
+ 0.2,
+ "#de77ae"
+ ],
+ [
+ 0.3,
+ "#f1b6da"
+ ],
+ [
+ 0.4,
+ "#fde0ef"
+ ],
+ [
+ 0.5,
+ "#f7f7f7"
+ ],
+ [
+ 0.6,
+ "#e6f5d0"
+ ],
+ [
+ 0.7,
+ "#b8e186"
+ ],
+ [
+ 0.8,
+ "#7fbc41"
+ ],
+ [
+ 0.9,
+ "#4d9221"
+ ],
+ [
+ 1,
+ "#276419"
+ ]
+ ],
+ "sequential": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "sequentialminus": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ]
+ },
+ "colorway": [
+ "#636efa",
+ "#EF553B",
+ "#00cc96",
+ "#ab63fa",
+ "#FFA15A",
+ "#19d3f3",
+ "#FF6692",
+ "#B6E880",
+ "#FF97FF",
+ "#FECB52"
+ ],
+ "font": {
+ "color": "#2a3f5f"
+ },
+ "geo": {
+ "bgcolor": "white",
+ "lakecolor": "white",
+ "landcolor": "#E5ECF6",
+ "showlakes": true,
+ "showland": true,
+ "subunitcolor": "white"
+ },
+ "hoverlabel": {
+ "align": "left"
+ },
+ "hovermode": "closest",
+ "mapbox": {
+ "style": "light"
+ },
+ "paper_bgcolor": "white",
+ "plot_bgcolor": "#E5ECF6",
+ "polar": {
+ "angularaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "radialaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "scene": {
+ "xaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "yaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "zaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "caxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "DDQN Training, Advantage Type: AdvantageType.MAX"
+ },
+ "xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 0.94
+ ],
+ "title": {
+ "text": "Episode"
+ }
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Score"
+ }
+ },
+ "yaxis2": {
+ "anchor": "x",
+ "overlaying": "y",
+ "side": "right",
+ "title": {
+ "text": "Exploration Rate"
+ }
+ }
+ }
+ },
+ "image/png": "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",
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running: jupytext --sync /Users/ryan.peach/Documents/ryanpeach/continuing_education/continuing_education/value_based_methods/duelingdqn/duelingdqn.ipynb\n",
+ "[jupytext] Reading /Users/ryan.peach/Documents/ryanpeach/continuing_education/continuing_education/value_based_methods/duelingdqn/duelingdqn.ipynb in format ipynb\n",
+ "[jupytext] Loading /Users/ryan.peach/Documents/ryanpeach/continuing_education/continuing_education/value_based_methods/duelingdqn/duelingdqn.py\n",
+ "[jupytext] Unchanged /Users/ryan.peach/Documents/ryanpeach/continuing_education/continuing_education/value_based_methods/duelingdqn/duelingdqn.ipynb\n",
+ "[jupytext] Unchanged /Users/ryan.peach/Documents/ryanpeach/continuing_education/continuing_education/value_based_methods/duelingdqn/duelingdqn.py\n"
+ ]
+ }
+ ],
+ "source": [
+ "import gym\n",
+ "from continuing_education.lib.experiments import ExperimentManager\n",
+ "import plotly.graph_objects as go\n",
+ "from plotly.subplots import make_subplots\n",
+ "from continuing_education.value_based_methods.dqn.dqn import exploration_rate_line\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " LR = 1e-3\n",
+ " GAMMA = 0.99999 # Cartpole benefits from a high gamma because the longer the pole is up, the higher the reward\n",
+ " HIDDEN_SIZES = [16, 16]\n",
+ " ADVANTAGE_HIDDEN_SIZES = [16]\n",
+ " VALUE_HIDDEN_SIZES = [16]\n",
+ " NUM_EPISODES = 500\n",
+ " MAX_T = 100\n",
+ " BATCH_SIZE = 64\n",
+ " MAX_MEMORY = 10000\n",
+ " EXPLORE_RATE_DECAY = 0.99\n",
+ " # Do this a few times to prove consistency\n",
+ " last_10_percent_mean = []\n",
+ " EM = ExperimentManager(\n",
+ " name=\"DDQN\",\n",
+ " description=\"Main Results\",\n",
+ " primary_metric=\"last_10_percent_mean\",\n",
+ " file=__this_file,\n",
+ " )\n",
+ "\n",
+ " for _ in range(3):\n",
+ " for adv_type in [AdvantageType.AVG, AdvantageType.MAX]:\n",
+ " env = gym.make(\"CartPole-v1\")\n",
+ " value_network = DuelingQLearningModel(\n",
+ " state_size=OBSERVATION_SPACE_SHAPE[0],\n",
+ " action_size=ACTION_SPACE_SIZE,\n",
+ " hidden_sizes=HIDDEN_SIZES,\n",
+ " advantage_hidden_sizes=ADVANTAGE_HIDDEN_SIZES,\n",
+ " value_hidden_sizes=VALUE_HIDDEN_SIZES,\n",
+ " adv_type=adv_type,\n",
+ " ).to(DEVICE)\n",
+ " optimizer = optim.Adam(value_network.parameters(), lr=LR)\n",
+ " scores = dqn_train(\n",
+ " env=env,\n",
+ " value_network=value_network,\n",
+ " optimizer=optimizer,\n",
+ " gamma=GAMMA,\n",
+ " num_episodes=NUM_EPISODES,\n",
+ " max_t=MAX_T,\n",
+ " memory=ActionReplayMemory(MAX_MEMORY),\n",
+ " batch_size=BATCH_SIZE,\n",
+ " exploration_rate_decay=EXPLORE_RATE_DECAY,\n",
+ " )\n",
+ " # Calculate the mean of the last 10 % of the scores\n",
+ " last_10_percent_mean.append(\n",
+ " sum(scores[int(NUM_EPISODES * 0.9) :]) / (NUM_EPISODES * 0.1)\n",
+ " )\n",
+ " _exploration_rate_line = exploration_rate_line(\n",
+ " explore_rate_decay=EXPLORE_RATE_DECAY,\n",
+ " start_value=1.0,\n",
+ " num_episodes=NUM_EPISODES,\n",
+ " )\n",
+ " fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
+ " fig.add_trace(\n",
+ " go.Scatter(\n",
+ " x=[i for i in range(NUM_EPISODES)],\n",
+ " y=_exploration_rate_line,\n",
+ " name=\"Exploration Rate\",\n",
+ " mode=\"lines\",\n",
+ " ),\n",
+ " secondary_y=True,\n",
+ " )\n",
+ " fig.add_trace(\n",
+ " go.Scatter(\n",
+ " x=[i for i in range(NUM_EPISODES)],\n",
+ " y=scores,\n",
+ " name=\"Score\",\n",
+ " mode=\"lines\",\n",
+ " ),\n",
+ " secondary_y=False,\n",
+ " )\n",
+ " fig.update_layout(title=f\"DDQN Training, Advantage Type: {adv_type}\")\n",
+ " fig.update_xaxes(title_text=\"Episode\")\n",
+ " fig.update_yaxes(title_text=\"Exploration Rate\", secondary_y=True)\n",
+ " fig.update_yaxes(title_text=\"Score\", secondary_y=False)\n",
+ " fig.show()\n",
+ " EM.commit(metrics={\"last_10_percent_mean\": last_10_percent_mean})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In my opinion, AVG advantage normalization worked better. MAX has a lot more variance than the AVG."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# References\n",
+ "\n",
+ "1. https://www.youtube.com/watch?v=wDVteayWWvU&t=371s\n",
+ "2. https://medium.com/@sainijagjit/understanding-dueling-dqn-a-deep-dive-into-reinforcement-learning-575f6fe4328c"
+ ]
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "formats": "ipynb,py:percent"
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/continuing_education/value_based_methods/duelingdqn/duelingdqn.py b/continuing_education/value_based_methods/duelingdqn/duelingdqn.py
new file mode 100644
index 0000000..0a19e0f
--- /dev/null
+++ b/continuing_education/value_based_methods/duelingdqn/duelingdqn.py
@@ -0,0 +1,297 @@
+# ---
+# jupyter:
+# jupytext:
+# formats: ipynb,py:percent
+# text_representation:
+# extension: .py
+# format_name: percent
+# format_version: '1.3'
+# jupytext_version: 1.16.7
+# kernelspec:
+# display_name: Python 3 (ipykernel)
+# language: python
+# name: python3
+# ---
+
+# %%
+# %load_ext autoreload
+# %autoreload 2
+
+# %%
+from pathlib import Path
+
+if __name__ == "__main__":
+ __this_file = (
+ Path().resolve() / "duelingdqn.ipynb"
+ ) # jupyter does not have __file__
+
+# %%
+import torch
+
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+if __name__ == "__main__":
+ print(DEVICE)
+
+# %%
+from torch import nn
+
+
+# %%
+from continuing_education.value_based_methods.dqn.dqn import QLearningModel
+from enum import Enum
+
+
+class AdvantageType(str, Enum):
+ AVG = "avg"
+ MAX = "max"
+
+
+class DuelingQLearningModel(QLearningModel):
+ def __init__(
+ self,
+ *,
+ state_size: int,
+ action_size: int,
+ hidden_sizes: list[int],
+ advantage_hidden_sizes: list[int],
+ value_hidden_sizes: list[int],
+ adv_type: AdvantageType,
+ ) -> None:
+ """
+ Notice that this is exactly the same as the Policy network from REINFORCE, because
+ we are still starting from the state and outputting an action. The difference is that
+ we will not softmax the output, because its not a probability distribution, but rather
+ a regressor that outputs the Q value of each action.
+ """
+ super().__init__(
+ state_size=state_size, action_size=action_size, hidden_sizes=hidden_sizes
+ )
+ assert len(hidden_sizes) > 0, "Need at least one hidden layer"
+ assert len(advantage_hidden_sizes) > 0, (
+ "Need at least one advantage hidden layer"
+ )
+ assert len(value_hidden_sizes) > 0, "Need at least one value hidden layer"
+ self.state_size = state_size
+ self.action_size = action_size
+ self.hidden_sizes = hidden_sizes
+ self.advantage_hidden_sizes = advantage_hidden_sizes
+ self.value_hidden_sizes = value_hidden_sizes
+ self.adv_type = adv_type
+
+ # Dimensions in the network are (batch_size, input_size, output_size)
+ network: list[nn.Module] = []
+ network.append(
+ nn.Linear(state_size, hidden_sizes[0])
+ ) # Shape: (:, state_size, hidden_sizes[0])
+ network.append(nn.ReLU())
+ for i in range(len(hidden_sizes) - 1):
+ network.append(
+ nn.Linear(hidden_sizes[i], hidden_sizes[i + 1])
+ ) # Shape: (:, hidden_sizes[i], hidden_sizes[i+1])
+ network.append(nn.ReLU())
+ self.network = nn.Sequential(*network).to(DEVICE)
+
+ # Now we are going to split into the value and advantage branches
+ # Advantage Network
+ advantage_network: list[nn.Module] = []
+ advantage_network.append(
+ nn.Linear(hidden_sizes[-1], advantage_hidden_sizes[0])
+ ) # Shape: (:, hidden_sizes[-1], advantage_hidden_sizes[0])
+ advantage_network.append(nn.ReLU())
+ for i in range(len(advantage_hidden_sizes) - 1):
+ advantage_network.append(
+ nn.Linear(advantage_hidden_sizes[i], advantage_hidden_sizes[i + 1])
+ ) # Shape: (:, advantage_hidden_sizes[i], advantage_hidden_sizes[i + 1])
+ advantage_network.append(nn.ReLU())
+ advantage_network.append(
+ nn.Linear(advantage_hidden_sizes[-1], action_size)
+ ) # Shape: (:, advantage_hidden_sizes[-1], action_size)
+ self.advantage_network = nn.Sequential(*advantage_network).to(DEVICE)
+
+ # Value Network
+ value_network: list[nn.Module] = []
+ value_network.append(
+ nn.Linear(hidden_sizes[-1], value_hidden_sizes[0])
+ ) # Shape: (:, hidden_sizes[-1], value_hidden_sizes[0])
+ value_network.append(nn.ReLU())
+ for i in range(len(advantage_hidden_sizes) - 1):
+ value_network.append(
+ nn.Linear(value_hidden_sizes[i], value_hidden_sizes[i + 1])
+ ) # Shape: (:, value_hidden_sizes[i], value_hidden_sizes[i + 1])
+ value_network.append(nn.ReLU())
+ value_network.append(
+ nn.Linear(value_hidden_sizes[-1], 1)
+ ) # Shape: (:, value_hidden_sizes[-1], 1)
+ self.value_network = nn.Sequential(*value_network).to(DEVICE)
+
+ def forward(self, state: torch.Tensor) -> torch.Tensor:
+ """Takes a state tensor and returns logits along the action space"""
+ state = state.to(DEVICE)
+ network_out = self.network(state)
+ advantage_out = self.advantage_network(network_out)
+ value_out = self.value_network(network_out)
+
+ # These "recreate" the Q function via a value function and an advantage function
+ if self.adv_type == AdvantageType.AVG:
+ advAverage = torch.mean(advantage_out, dim=1, keepdim=True)
+ q = value_out + advantage_out - advAverage
+ elif self.adv_type == AdvantageType.MAX:
+ advMax, _ = torch.max(advantage_out, dim=1, keepdim=True)
+ q = value_out + advantage_out - advMax
+ else:
+ raise KeyError(f"{self.adv_type} not recognized")
+ return q # Shape: (:, action_size)
+
+
+# %% [markdown]
+# Testing the new DDQN network the same as we did in the DQN notebook. For some reason this doesn't always get straight 10's as last time. We will forgive it.
+
+# %%
+from continuing_education.policy_gradient_methods.reinforce.reinforce import MockEnv
+from continuing_education.value_based_methods.dqn.dqn import (
+ dqn_train,
+ ActionReplayMemory,
+)
+from torch import optim
+
+
+def test_ddqn_train() -> None:
+ """Test the reinforce training loop on the mock environment."""
+ env = MockEnv(max_steps=10)
+ value_network = DuelingQLearningModel(
+ state_size=1,
+ action_size=2,
+ hidden_sizes=[16, 16],
+ advantage_hidden_sizes=[16],
+ value_hidden_sizes=[16],
+ adv_type=AdvantageType.MAX,
+ )
+ optimizer = optim.Adam(value_network.parameters(), lr=1e-3)
+ memory = ActionReplayMemory(max_size=1000)
+ scores = dqn_train(
+ env=env,
+ value_network=value_network,
+ optimizer=optimizer,
+ memory=memory,
+ gamma=0.999,
+ num_episodes=100,
+ max_t=10,
+ batch_size=50,
+ exploration_rate_decay=0.96,
+ )
+ assert sum([score == 10 for score in scores[90:]]) >= 8, (
+ f"The last 10 scores should be 10, got: {scores[90:]}"
+ )
+
+
+if __name__ == "__main__":
+ for _ in range(3):
+ test_ddqn_train()
+ print("test_ddqn_train passed")
+
+# %%
+from continuing_education.policy_gradient_methods.reinforce.reinforce import (
+ get_environment_space,
+)
+
+
+if __name__ == "__main__":
+ OBSERVATION_SPACE_SHAPE, ACTION_SPACE_SIZE = get_environment_space("CartPole-v1")
+
+# %% [markdown]
+# Lastly, because Q Learning can not learn stochastic policies, we need to incentivize the network to explore during training or else we will not learn a good value function. We do this by adding noise to the action selection during training, and reducing it over time. This is called epsilon greedy exploration. We will start with an epsilon of 1, meaning we will always take a random action, and decay it to 0 exponentially over the course of training. You want to pick a discount factor which converges to 0 just before the end of training usually.
+
+# %%
+import gym
+from continuing_education.lib.experiments import ExperimentManager
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from continuing_education.value_based_methods.dqn.dqn import exploration_rate_line
+
+if __name__ == "__main__":
+ LR = 1e-3
+ GAMMA = 0.99999 # Cartpole benefits from a high gamma because the longer the pole is up, the higher the reward
+ HIDDEN_SIZES = [16, 16]
+ ADVANTAGE_HIDDEN_SIZES = [16]
+ VALUE_HIDDEN_SIZES = [16]
+ NUM_EPISODES = 500
+ MAX_T = 100
+ BATCH_SIZE = 64
+ MAX_MEMORY = 10000
+ EXPLORE_RATE_DECAY = 0.99
+ # Do this a few times to prove consistency
+ last_10_percent_mean = []
+ EM = ExperimentManager(
+ name="DDQN",
+ description="Main Results",
+ primary_metric="last_10_percent_mean",
+ file=__this_file,
+ )
+
+ for _ in range(3):
+ for adv_type in [AdvantageType.AVG, AdvantageType.MAX]:
+ env = gym.make("CartPole-v1")
+ value_network = DuelingQLearningModel(
+ state_size=OBSERVATION_SPACE_SHAPE[0],
+ action_size=ACTION_SPACE_SIZE,
+ hidden_sizes=HIDDEN_SIZES,
+ advantage_hidden_sizes=ADVANTAGE_HIDDEN_SIZES,
+ value_hidden_sizes=VALUE_HIDDEN_SIZES,
+ adv_type=adv_type,
+ ).to(DEVICE)
+ optimizer = optim.Adam(value_network.parameters(), lr=LR)
+ scores = dqn_train(
+ env=env,
+ value_network=value_network,
+ optimizer=optimizer,
+ gamma=GAMMA,
+ num_episodes=NUM_EPISODES,
+ max_t=MAX_T,
+ memory=ActionReplayMemory(MAX_MEMORY),
+ batch_size=BATCH_SIZE,
+ exploration_rate_decay=EXPLORE_RATE_DECAY,
+ )
+ # Calculate the mean of the last 10 % of the scores
+ last_10_percent_mean.append(
+ sum(scores[int(NUM_EPISODES * 0.9) :]) / (NUM_EPISODES * 0.1)
+ )
+ _exploration_rate_line = exploration_rate_line(
+ explore_rate_decay=EXPLORE_RATE_DECAY,
+ start_value=1.0,
+ num_episodes=NUM_EPISODES,
+ )
+ fig = make_subplots(specs=[[{"secondary_y": True}]])
+ fig.add_trace(
+ go.Scatter(
+ x=[i for i in range(NUM_EPISODES)],
+ y=_exploration_rate_line,
+ name="Exploration Rate",
+ mode="lines",
+ ),
+ secondary_y=True,
+ )
+ fig.add_trace(
+ go.Scatter(
+ x=[i for i in range(NUM_EPISODES)],
+ y=scores,
+ name="Score",
+ mode="lines",
+ ),
+ secondary_y=False,
+ )
+ fig.update_layout(title=f"DDQN Training, Advantage Type: {adv_type}")
+ fig.update_xaxes(title_text="Episode")
+ fig.update_yaxes(title_text="Exploration Rate", secondary_y=True)
+ fig.update_yaxes(title_text="Score", secondary_y=False)
+ fig.show()
+ EM.commit(metrics={"last_10_percent_mean": last_10_percent_mean})
+
+# %% [markdown]
+# In my opinion, AVG advantage normalization worked better. MAX has a lot more variance than the AVG.
+
+# %% [markdown]
+# # References
+#
+# 1. https://www.youtube.com/watch?v=wDVteayWWvU&t=371s
+# 2. https://medium.com/@sainijagjit/understanding-dueling-dqn-a-deep-dive-into-reinforcement-learning-575f6fe4328c
diff --git a/notes/pages/actor-critic.md b/notes/pages/actor-critic.md
new file mode 100644
index 0000000..0234214
--- /dev/null
+++ b/notes/pages/actor-critic.md
@@ -0,0 +1,12 @@
+---
+alias: actor critic, a2c, a3c
+---
+- What is the [[value function]] used for in [[actor critic]] methods? #card
+ - The [[value function]] is used to estimate the *average* expected return from a given state.
+ - It could theoretically be a Q-function, but in practice, it is often a state-value function using [[TD]] error.
+- What is the [[advantage]] function used for in [[actor critic]] methods? #card
+ - The difference between the expected reward from a state-action pair (Q) and the average expected reward from just the state (V).
+ - $A(s, a) = Q(s, a) - V(s)$
+ - It is used to normalize the [[policy gradient]], as well as to push the [[policy gradient]] towards actions that are better than average and away from actions that are worse than average.
+- What is the training loop for [[a2c]] [[actor critic]]? #card
+- What is the difference between [[a2c]] and [[a3c]]? #card
diff --git a/notes/pages/advantage.md b/notes/pages/advantage.md
new file mode 100644
index 0000000..30bd228
--- /dev/null
+++ b/notes/pages/advantage.md
@@ -0,0 +1,13 @@
+---
+alias: advantage function
+---
+
+- What shape is the [[advantage]] function? #card
+ - $A(s,a) \in \mathbb{R}^{|A|}$, where $|A|$ is the number of actions.
+ - It works in a fixed integer number of actions.
+ - Same shape as the Q-value function.
+- What is the intuition behind the [[advantage]] function? #card
+ - The [[advantage]] function is a measure of how much better an action is compared to the average action in a given state.
+ - Learning relative [[advantage]] is easier and has less variance than learning absolute values. [[Advantage]] is more relevant to decision making via argmax than absolute values.
+- Define the [[advantage]] function in terms of the Q-value function and the value function. #card
+ - $A(s,a) = Q(s,a) - V(s)$
diff --git a/notes/pages/dqn.md b/notes/pages/dqn.md
index 407112d..6a826ad 100644
--- a/notes/pages/dqn.md
+++ b/notes/pages/dqn.md
@@ -18,7 +18,7 @@
* QLearning uses an argmax to select the best action whereas [[REINFORCE]] uses a softmax sample to select an action
* QLearning has an epsilon greedy [[policy]] whereas [[REINFORCE]] has a stochastic [[policy]]
* Define epsilon greedy [[policy]] #card
- * An epsilon greedy [[policy]] is a [[policy]] that selects the best action with [[probability]] $1 - \epsilon$ and a random action with [[probability]] $\epsilon$
+ * An epsilon greedy [[policy]] is a [[policy]] that selects the best action with probability $1 - \epsilon$ and a random action with probability $\epsilon$
* What are some differences in the `train` method between `QLearning` and `REINFORCE` #card
* QLearning trains on each step whereas [[REINFORCE]] trains at the end of each episode
* [[REINFORCE]] needs a whole trajectory to train, because it operates on real cumulative rewards, whereas QLearning can train on each step because it operates on predicted cumulative rewards
@@ -27,7 +27,7 @@
* [[REINFORCE]] needs a whole trajectory to train, because it operates on real cumulative rewards, whereas QLearning can train on each step because it operates on predicted cumulative rewards
* This is because the bellman equation requires a mixture of one real reward and one predicted reward from the network to properly train
* What are the differences between exploration rate and temperature in Q-learning and REINFORCE? #card
- * Exploration rate is a [[probability]] of taking a random action
+ * Exploration rate is a probability of taking a random action
* Temperature is a parameter in the softmax function that controls the stochasticity of the [[policy]]
* Exploration either happens or does not happen and is not controlled by the neural network at all
* Temperature just controls the output of the network. If the network is very confident in one action, it will still take that action with high probability, but if the network is unsure, it will take a random action with some probability. Therefore you don't need to decay temperature, but you do need to decay exploration rate.
diff --git a/notes/pages/duelingdqn.md b/notes/pages/duelingdqn.md
new file mode 100644
index 0000000..f158df0
--- /dev/null
+++ b/notes/pages/duelingdqn.md
@@ -0,0 +1,8 @@
+---
+alias: dueling deep q network
+---
+
+- The Dueling [[DQN]] architecture is a modification of the standard [[DQN]] architecture that separates the [[value function]] and [[advantage function]] into two separate streams.
+- Because the [[advantage]] function can be defined as $A(s,a) = Q(s,a) - V(s)$, we can re-arrange this to get the Q-value function as $Q(s,a) = A(s,a) - V(s)$.
+ - As such, we can create two networks instead of one, a $V(s)$ network and an $A(s,a)$ network, and combine them to get the Q-value function.
+ - This has the effect of constraining the bias of each network, which can help with convergence.
diff --git a/notes/pages/log probability.md b/notes/pages/log probability.md
deleted file mode 100644
index e69de29..0000000
diff --git a/notes/pages/log-probability.md b/notes/pages/log-probability.md
new file mode 100644
index 0000000..971b862
--- /dev/null
+++ b/notes/pages/log-probability.md
@@ -0,0 +1,3 @@
+---
+alias: log probability, log prob
+---
diff --git a/notes/pages/policy gradient.md b/notes/pages/policy gradient.md
deleted file mode 100644
index 39cdd0d..0000000
--- a/notes/pages/policy gradient.md
+++ /dev/null
@@ -1 +0,0 @@
--
diff --git a/notes/pages/policy-gradient.md b/notes/pages/policy-gradient.md
new file mode 100644
index 0000000..ca64048
--- /dev/null
+++ b/notes/pages/policy-gradient.md
@@ -0,0 +1,3 @@
+---
+alias: policy gradient, pg
+---
diff --git a/notes/pages/probability.md b/notes/pages/probability.md
deleted file mode 100644
index e69de29..0000000
diff --git a/notes/pages/reinforce.md b/notes/pages/reinforce.md
index c13735e..7949334 100644
--- a/notes/pages/reinforce.md
+++ b/notes/pages/reinforce.md
@@ -3,23 +3,23 @@
- $L(\theta)$: The loss function
- $T$: The number of time steps in the episode
- $G_t$: Cumulative discounted future reward at time step $t$
- - $\pi_{\theta}(a_t | s_t)$: The [[probability]] of taking action $a_t$ in state $s_t$ under [[policy]] $\pi_{\theta}$
+ - $\pi_{\theta}(a_t | s_t)$: The probability of taking action $a_t$ in state $s_t$ under [[policy]] $\pi_{\theta}$
- $\Delta \theta = \alpha r \frac{\partial \log \pi_{\theta}(s, a)}{\partial \theta}$
- $\Delta \theta$: The update to the [[policy]] parameters
- $\Delta$ is the first or second [[gradient]]? What is it called? #card
- Second
- [[Laplacian]] operator
- What is the difference between a [[Henessian]] and a [[Laplacian]]? #card
- - Henessians produce a matrix of second derivatives, while Laplacians produce a scalar. Both are second order [[derivatives]].
- - $\alpha$: The [[learning rate]]
+ - Henessians produce a matrix of second derivatives, while Laplacians produce a scalar. Both are second order derivatives.
+ - $\alpha$: The learning rate
- $r$: The reward
- $\partial{\log \pi_{\theta}(s, a)}$: The [[gradient]] of the [[log probability]] of taking action $a$ in state $s$ under [[policy]] $\pi_{\theta}$
- What is the [[policy gradient]] theorem? #card
- For any differentiable [[policy]] and for any [[policy]] objective function, the [[policy gradient]] is: $\nabla_{\theta} J(\theta) = \mathbb{E}_{\pi_{\theta}}[\nabla_{\theta} \log \pi_{\theta}(a_t | s_t) R(\tau)]$
- What does the symbol $\nabla$ mean? #card
- - The [[gradient]], which is a vector of single partial [[derivatives]] in each dimension of the input space.
+ - The [[gradient]], which is a vector of single partial derivatives in each dimension of the input space.
- $J(\theta)$: The objective function to maximize
- - $\pi_{\theta}(a_t | s_t)$: The [[probability]] of taking action $a_t$ in state $s_t$ under [[policy]] $\pi_{\theta}$
+ - $\pi_{\theta}(a_t | s_t)$: The probability of taking action $a_t$ in state $s_t$ under [[policy]] $\pi_{\theta}$
- $R(\tau)$: The return of a trajectory $\tau$, which is often formulated as cumulative discounted future rewards.
- How is this different from the [[REINFORCE]] scoring function? #card
- It's more general
@@ -36,11 +36,11 @@
- Update the [[policy]] parameters $\theta$ with the [[gradient]]
- What is $\pi_{\theta}(a | s)$? #card
- The function which is learned by the [[REINFORCE]] algorithm
- - The [[probability]] of taking action $a$ in state $s$ under [[policy]] $\pi_{\theta}$
+ - The probability of taking action $a$ in state $s$ under [[policy]] $\pi_{\theta}$
- Why do you need to normalize the rewards in [[REINFORCE]]? #card
- Since the rewards are arbitrary and directly part of the objective/loss function, they are normalized to make the optimization more stable.
-- What is a multinomial [[distribution]]? #card
- - A [[probability]] [[distribution]] over a discrete number of possible outcomes, where each outcome has a [[probability]] associated with it. Like a dice roll, where each face has a [[probability]] of being rolled.
+- What is a multinomial distribution? #card
+ - A probability distribution over a discrete number of possible outcomes, where each outcome has a probability associated with it. Like a dice roll, where each face has a probability of being rolled.
- What are some advantages of [[policy gradient]] methods over [[value based methods]]? #card
- They can learn stochastic policies
- They can learn policies in high-dimensional or continuous action spaces
@@ -53,6 +53,6 @@
- They can be computationally expensive
- What is the softmax equation with temperature? #card
- $P(i) = \frac{e^{Z_i/T}}{\sum_{j} e^{Z_j/T}}$
- - $P(i)$: The [[probability]] of outcome $i$
+ - $P(i)$: The probability of outcome $i$
- $Z_i$: The logit of outcome $i$
- $T$: The temperature parameter
diff --git a/notes/pages/learning rate.md b/notes/pages/td.md
similarity index 100%
rename from notes/pages/learning rate.md
rename to notes/pages/td.md
diff --git a/notes/pages/value based methods.md b/notes/pages/value based methods.md
deleted file mode 100644
index 39cdd0d..0000000
--- a/notes/pages/value based methods.md
+++ /dev/null
@@ -1 +0,0 @@
--
diff --git a/notes/pages/value-based-methods.md b/notes/pages/value-based-methods.md
new file mode 100644
index 0000000..38167ae
--- /dev/null
+++ b/notes/pages/value-based-methods.md
@@ -0,0 +1,3 @@
+---
+alias: value-based methods, value based methods, value methods, value function
+---
diff --git a/pyproject.toml b/pyproject.toml
index 95d6bc6..25f2f35 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -57,7 +57,9 @@ reportPossiblyUnboundVariable = false # This happens a lot with juyter notebooks
[tool.uv]
dev-dependencies = [
+ "ipywidgets>=8.1.5",
"jupytext>=1.16.7",
+ "notebook>=7.3.2",
]
[tool.uv.sources]
diff --git a/uv.lock b/uv.lock
index 0cd3972..47f2d8e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -14,6 +14,75 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
]
+[[package]]
+name = "anyio"
+version = "4.8.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "idna" },
+ { name = "sniffio" },
+ { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a3/73/199a98fc2dae33535d6b8e8e6ec01f8c1d76c9adb096c6b7d64823038cde/anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a", size = 181126 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/46/eb/e7f063ad1fec6b3178a3cd82d1a3c4de82cccf283fc42746168188e1cdd5/anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a", size = 96041 },
+]
+
+[[package]]
+name = "appnope"
+version = "0.1.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321 },
+]
+
+[[package]]
+name = "argon2-cffi"
+version = "23.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "argon2-cffi-bindings" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/31/fa/57ec2c6d16ecd2ba0cf15f3c7d1c3c2e7b5fcb83555ff56d7ab10888ec8f/argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08", size = 42798 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/a4/6a/e8a041599e78b6b3752da48000b14c8d1e8a04ded09c88c714ba047f34f5/argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea", size = 15124 },
+]
+
+[[package]]
+name = "argon2-cffi-bindings"
+version = "21.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "cffi" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b9/e9/184b8ccce6683b0aa2fbb7ba5683ea4b9c5763f1356347f1312c32e3c66e/argon2-cffi-bindings-21.2.0.tar.gz", hash = "sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3", size = 1779911 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/d4/13/838ce2620025e9666aa8f686431f67a29052241692a3dd1ae9d3692a89d3/argon2_cffi_bindings-21.2.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:ccb949252cb2ab3a08c02024acb77cfb179492d5701c7cbdbfd776124d4d2367", size = 29658 },
+ { url = "https://files.pythonhosted.org/packages/b3/02/f7f7bb6b6af6031edb11037639c697b912e1dea2db94d436e681aea2f495/argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9524464572e12979364b7d600abf96181d3541da11e23ddf565a32e70bd4dc0d", size = 80583 },
+ { url = "https://files.pythonhosted.org/packages/ec/f7/378254e6dd7ae6f31fe40c8649eea7d4832a42243acaf0f1fff9083b2bed/argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b746dba803a79238e925d9046a63aa26bf86ab2a2fe74ce6b009a1c3f5c8f2ae", size = 86168 },
+ { url = "https://files.pythonhosted.org/packages/74/f6/4a34a37a98311ed73bb80efe422fed95f2ac25a4cacc5ae1d7ae6a144505/argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:58ed19212051f49a523abb1dbe954337dc82d947fb6e5a0da60f7c8471a8476c", size = 82709 },
+ { url = "https://files.pythonhosted.org/packages/74/2b/73d767bfdaab25484f7e7901379d5f8793cccbb86c6e0cbc4c1b96f63896/argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:bd46088725ef7f58b5a1ef7ca06647ebaf0eb4baff7d1d0d177c6cc8744abd86", size = 83613 },
+ { url = "https://files.pythonhosted.org/packages/4f/fd/37f86deef67ff57c76f137a67181949c2d408077e2e3dd70c6c42912c9bf/argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_i686.whl", hash = "sha256:8cd69c07dd875537a824deec19f978e0f2078fdda07fd5c42ac29668dda5f40f", size = 84583 },
+ { url = "https://files.pythonhosted.org/packages/6f/52/5a60085a3dae8fded8327a4f564223029f5f54b0cb0455a31131b5363a01/argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:f1152ac548bd5b8bcecfb0b0371f082037e47128653df2e8ba6e914d384f3c3e", size = 88475 },
+ { url = "https://files.pythonhosted.org/packages/8b/95/143cd64feb24a15fa4b189a3e1e7efbaeeb00f39a51e99b26fc62fbacabd/argon2_cffi_bindings-21.2.0-cp36-abi3-win32.whl", hash = "sha256:603ca0aba86b1349b147cab91ae970c63118a0f30444d4bc80355937c950c082", size = 27698 },
+ { url = "https://files.pythonhosted.org/packages/37/2c/e34e47c7dee97ba6f01a6203e0383e15b60fb85d78ac9a15cd066f6fe28b/argon2_cffi_bindings-21.2.0-cp36-abi3-win_amd64.whl", hash = "sha256:b2ef1c30440dbbcba7a5dc3e319408b59676e2e039e2ae11a8775ecf482b192f", size = 30817 },
+ { url = "https://files.pythonhosted.org/packages/5a/e4/bf8034d25edaa495da3c8a3405627d2e35758e44ff6eaa7948092646fdcc/argon2_cffi_bindings-21.2.0-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:e415e3f62c8d124ee16018e491a009937f8cf7ebf5eb430ffc5de21b900dad93", size = 53104 },
+]
+
+[[package]]
+name = "arrow"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "python-dateutil" },
+ { name = "types-python-dateutil" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/2e/00/0f6e8fcdb23ea632c866620cc872729ff43ed91d284c866b515c6342b173/arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85", size = 131960 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/f8/ed/e97229a566617f2ae958a6b13e7cc0f585470eac730a73e9e82c32a3cdd2/arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80", size = 66419 },
+]
+
[[package]]
name = "asttokens"
version = "3.0.0"
@@ -23,6 +92,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/25/8a/c46dcc25341b5bce5472c718902eb3d38600a903b14fa6aeecef3f21a46f/asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2", size = 26918 },
]
+[[package]]
+name = "async-lru"
+version = "2.0.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/80/e2/2b4651eff771f6fd900d233e175ddc5e2be502c7eb62c0c42f975c6d36cd/async-lru-2.0.4.tar.gz", hash = "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627", size = 10019 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/fa/9f/3c3503693386c4b0f245eaf5ca6198e3b28879ca0a40bde6b0e319793453/async_lru-2.0.4-py3-none-any.whl", hash = "sha256:ff02944ce3c288c5be660c42dbcca0742b32c3b279d6dceda655190240b99224", size = 6111 },
+]
+
[[package]]
name = "attrs"
version = "25.1.0"
@@ -32,6 +110,45 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/fc/30/d4986a882011f9df997a55e6becd864812ccfcd821d64aac8570ee39f719/attrs-25.1.0-py3-none-any.whl", hash = "sha256:c75a69e28a550a7e93789579c22aa26b0f5b83b75dc4e08fe092980051e1090a", size = 63152 },
]
+[[package]]
+name = "babel"
+version = "2.17.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/6b/d52e42361e1aa00709585ecc30b3f9684b3ab62530771402248b1b1d6240/babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d", size = 9951852 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537 },
+]
+
+[[package]]
+name = "beautifulsoup4"
+version = "4.13.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "soupsieve" },
+ { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f0/3c/adaf39ce1fb4afdd21b611e3d530b183bb7759c9b673d60db0e347fd4439/beautifulsoup4-4.13.3.tar.gz", hash = "sha256:1bd32405dacc920b42b83ba01644747ed77456a65760e285fbc47633ceddaf8b", size = 619516 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/f9/49/6abb616eb3cbab6a7cca303dc02fdf3836de2e0b834bf966a7f5271a34d8/beautifulsoup4-4.13.3-py3-none-any.whl", hash = "sha256:99045d7d3f08f91f0d656bc9b7efbae189426cd913d830294a15eefa0ea4df16", size = 186015 },
+]
+
+[[package]]
+name = "bleach"
+version = "6.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "webencodings" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/76/9a/0e33f5054c54d349ea62c277191c020c2d6ef1d65ab2cb1993f91ec846d1/bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f", size = 203083 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/fc/55/96142937f66150805c25c4d0f31ee4132fd33497753400734f9dfdcbdc66/bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e", size = 163406 },
+]
+
+[package.optional-dependencies]
+css = [
+ { name = "tinycss2" },
+]
+
[[package]]
name = "certifi"
version = "2025.1.31"
@@ -41,6 +158,51 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/38/fc/bce832fd4fd99766c04d1ee0eead6b0ec6486fb100ae5e74c1d91292b982/certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe", size = 166393 },
]
+[[package]]
+name = "cffi"
+version = "1.17.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "pycparser" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fc/97/c783634659c2920c3fc70419e3af40972dbaf758daa229a7d6ea6135c90d/cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824", size = 516621 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/6b/f4/927e3a8899e52a27fa57a48607ff7dc91a9ebe97399b357b85a0c7892e00/cffi-1.17.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a45e3c6913c5b87b3ff120dcdc03f6131fa0065027d0ed7ee6190736a74cd401", size = 182264 },
+ { url = "https://files.pythonhosted.org/packages/6c/f5/6c3a8efe5f503175aaddcbea6ad0d2c96dad6f5abb205750d1b3df44ef29/cffi-1.17.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:30c5e0cb5ae493c04c8b42916e52ca38079f1b235c2f8ae5f4527b963c401caf", size = 178651 },
+ { url = "https://files.pythonhosted.org/packages/94/dd/a3f0118e688d1b1a57553da23b16bdade96d2f9bcda4d32e7d2838047ff7/cffi-1.17.1-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f75c7ab1f9e4aca5414ed4d8e5c0e303a34f4421f8a0d47a4d019ceff0ab6af4", size = 445259 },
+ { url = "https://files.pythonhosted.org/packages/2e/ea/70ce63780f096e16ce8588efe039d3c4f91deb1dc01e9c73a287939c79a6/cffi-1.17.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a1ed2dd2972641495a3ec98445e09766f077aee98a1c896dcb4ad0d303628e41", size = 469200 },
+ { url = "https://files.pythonhosted.org/packages/1c/a0/a4fa9f4f781bda074c3ddd57a572b060fa0df7655d2a4247bbe277200146/cffi-1.17.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:46bf43160c1a35f7ec506d254e5c890f3c03648a4dbac12d624e4490a7046cd1", size = 477235 },
+ { url = "https://files.pythonhosted.org/packages/62/12/ce8710b5b8affbcdd5c6e367217c242524ad17a02fe5beec3ee339f69f85/cffi-1.17.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a24ed04c8ffd54b0729c07cee15a81d964e6fee0e3d4d342a27b020d22959dc6", size = 459721 },
+ { url = "https://files.pythonhosted.org/packages/ff/6b/d45873c5e0242196f042d555526f92aa9e0c32355a1be1ff8c27f077fd37/cffi-1.17.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:610faea79c43e44c71e1ec53a554553fa22321b65fae24889706c0a84d4ad86d", size = 467242 },
+ { url = "https://files.pythonhosted.org/packages/1a/52/d9a0e523a572fbccf2955f5abe883cfa8bcc570d7faeee06336fbd50c9fc/cffi-1.17.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:a9b15d491f3ad5d692e11f6b71f7857e7835eb677955c00cc0aefcd0669adaf6", size = 477999 },
+ { url = "https://files.pythonhosted.org/packages/44/74/f2a2460684a1a2d00ca799ad880d54652841a780c4c97b87754f660c7603/cffi-1.17.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:de2ea4b5833625383e464549fec1bc395c1bdeeb5f25c4a3a82b5a8c756ec22f", size = 454242 },
+ { url = "https://files.pythonhosted.org/packages/f8/4a/34599cac7dfcd888ff54e801afe06a19c17787dfd94495ab0c8d35fe99fb/cffi-1.17.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:fc48c783f9c87e60831201f2cce7f3b2e4846bf4d8728eabe54d60700b318a0b", size = 478604 },
+ { url = "https://files.pythonhosted.org/packages/34/33/e1b8a1ba29025adbdcda5fb3a36f94c03d771c1b7b12f726ff7fef2ebe36/cffi-1.17.1-cp311-cp311-win32.whl", hash = "sha256:85a950a4ac9c359340d5963966e3e0a94a676bd6245a4b55bc43949eee26a655", size = 171727 },
+ { url = "https://files.pythonhosted.org/packages/3d/97/50228be003bb2802627d28ec0627837ac0bf35c90cf769812056f235b2d1/cffi-1.17.1-cp311-cp311-win_amd64.whl", hash = "sha256:caaf0640ef5f5517f49bc275eca1406b0ffa6aa184892812030f04c2abf589a0", size = 181400 },
+ { url = "https://files.pythonhosted.org/packages/5a/84/e94227139ee5fb4d600a7a4927f322e1d4aea6fdc50bd3fca8493caba23f/cffi-1.17.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:805b4371bf7197c329fcb3ead37e710d1bca9da5d583f5073b799d5c5bd1eee4", size = 183178 },
+ { url = "https://files.pythonhosted.org/packages/da/ee/fb72c2b48656111c4ef27f0f91da355e130a923473bf5ee75c5643d00cca/cffi-1.17.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:733e99bc2df47476e3848417c5a4540522f234dfd4ef3ab7fafdf555b082ec0c", size = 178840 },
+ { url = "https://files.pythonhosted.org/packages/cc/b6/db007700f67d151abadf508cbfd6a1884f57eab90b1bb985c4c8c02b0f28/cffi-1.17.1-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1257bdabf294dceb59f5e70c64a3e2f462c30c7ad68092d01bbbfb1c16b1ba36", size = 454803 },
+ { url = "https://files.pythonhosted.org/packages/1a/df/f8d151540d8c200eb1c6fba8cd0dfd40904f1b0682ea705c36e6c2e97ab3/cffi-1.17.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:da95af8214998d77a98cc14e3a3bd00aa191526343078b530ceb0bd710fb48a5", size = 478850 },
+ { url = "https://files.pythonhosted.org/packages/28/c0/b31116332a547fd2677ae5b78a2ef662dfc8023d67f41b2a83f7c2aa78b1/cffi-1.17.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d63afe322132c194cf832bfec0dc69a99fb9bb6bbd550f161a49e9e855cc78ff", size = 485729 },
+ { url = "https://files.pythonhosted.org/packages/91/2b/9a1ddfa5c7f13cab007a2c9cc295b70fbbda7cb10a286aa6810338e60ea1/cffi-1.17.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f79fc4fc25f1c8698ff97788206bb3c2598949bfe0fef03d299eb1b5356ada99", size = 471256 },
+ { url = "https://files.pythonhosted.org/packages/b2/d5/da47df7004cb17e4955df6a43d14b3b4ae77737dff8bf7f8f333196717bf/cffi-1.17.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b62ce867176a75d03a665bad002af8e6d54644fad99a3c70905c543130e39d93", size = 479424 },
+ { url = "https://files.pythonhosted.org/packages/0b/ac/2a28bcf513e93a219c8a4e8e125534f4f6db03e3179ba1c45e949b76212c/cffi-1.17.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:386c8bf53c502fff58903061338ce4f4950cbdcb23e2902d86c0f722b786bbe3", size = 484568 },
+ { url = "https://files.pythonhosted.org/packages/d4/38/ca8a4f639065f14ae0f1d9751e70447a261f1a30fa7547a828ae08142465/cffi-1.17.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:4ceb10419a9adf4460ea14cfd6bc43d08701f0835e979bf821052f1805850fe8", size = 488736 },
+ { url = "https://files.pythonhosted.org/packages/86/c5/28b2d6f799ec0bdecf44dced2ec5ed43e0eb63097b0f58c293583b406582/cffi-1.17.1-cp312-cp312-win32.whl", hash = "sha256:a08d7e755f8ed21095a310a693525137cfe756ce62d066e53f502a83dc550f65", size = 172448 },
+ { url = "https://files.pythonhosted.org/packages/50/b9/db34c4755a7bd1cb2d1603ac3863f22bcecbd1ba29e5ee841a4bc510b294/cffi-1.17.1-cp312-cp312-win_amd64.whl", hash = "sha256:51392eae71afec0d0c8fb1a53b204dbb3bcabcb3c9b807eedf3e1e6ccf2de903", size = 181976 },
+ { url = "https://files.pythonhosted.org/packages/8d/f8/dd6c246b148639254dad4d6803eb6a54e8c85c6e11ec9df2cffa87571dbe/cffi-1.17.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f3a2b4222ce6b60e2e8b337bb9596923045681d71e5a082783484d845390938e", size = 182989 },
+ { url = "https://files.pythonhosted.org/packages/8b/f1/672d303ddf17c24fc83afd712316fda78dc6fce1cd53011b839483e1ecc8/cffi-1.17.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:0984a4925a435b1da406122d4d7968dd861c1385afe3b45ba82b750f229811e2", size = 178802 },
+ { url = "https://files.pythonhosted.org/packages/0e/2d/eab2e858a91fdff70533cab61dcff4a1f55ec60425832ddfdc9cd36bc8af/cffi-1.17.1-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d01b12eeeb4427d3110de311e1774046ad344f5b1a7403101878976ecd7a10f3", size = 454792 },
+ { url = "https://files.pythonhosted.org/packages/75/b2/fbaec7c4455c604e29388d55599b99ebcc250a60050610fadde58932b7ee/cffi-1.17.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:706510fe141c86a69c8ddc029c7910003a17353970cff3b904ff0686a5927683", size = 478893 },
+ { url = "https://files.pythonhosted.org/packages/4f/b7/6e4a2162178bf1935c336d4da8a9352cccab4d3a5d7914065490f08c0690/cffi-1.17.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:de55b766c7aa2e2a3092c51e0483d700341182f08e67c63630d5b6f200bb28e5", size = 485810 },
+ { url = "https://files.pythonhosted.org/packages/c7/8a/1d0e4a9c26e54746dc08c2c6c037889124d4f59dffd853a659fa545f1b40/cffi-1.17.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c59d6e989d07460165cc5ad3c61f9fd8f1b4796eacbd81cee78957842b834af4", size = 471200 },
+ { url = "https://files.pythonhosted.org/packages/26/9f/1aab65a6c0db35f43c4d1b4f580e8df53914310afc10ae0397d29d697af4/cffi-1.17.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd398dbc6773384a17fe0d3e7eeb8d1a21c2200473ee6806bb5e6a8e62bb73dd", size = 479447 },
+ { url = "https://files.pythonhosted.org/packages/5f/e4/fb8b3dd8dc0e98edf1135ff067ae070bb32ef9d509d6cb0f538cd6f7483f/cffi-1.17.1-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:3edc8d958eb099c634dace3c7e16560ae474aa3803a5df240542b305d14e14ed", size = 484358 },
+ { url = "https://files.pythonhosted.org/packages/f1/47/d7145bf2dc04684935d57d67dff9d6d795b2ba2796806bb109864be3a151/cffi-1.17.1-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:72e72408cad3d5419375fc87d289076ee319835bdfa2caad331e377589aebba9", size = 488469 },
+ { url = "https://files.pythonhosted.org/packages/bf/ee/f94057fa6426481d663b88637a9a10e859e492c73d0384514a17d78ee205/cffi-1.17.1-cp313-cp313-win32.whl", hash = "sha256:e03eab0a8677fa80d646b5ddece1cbeaf556c313dcfac435ba11f107ba117b5d", size = 172475 },
+ { url = "https://files.pythonhosted.org/packages/7c/fc/6a8cb64e5f0324877d503c854da15d76c1e50eb722e320b15345c4d0c6de/cffi-1.17.1-cp313-cp313-win_amd64.whl", hash = "sha256:f6a16c31041f09ead72d69f583767292f750d24913dadacf5756b966aacb3f1a", size = 182009 },
+]
+
[[package]]
name = "charset-normalizer"
version = "3.4.1"
@@ -107,6 +269,18 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
]
+[[package]]
+name = "comm"
+version = "0.2.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e9/a8/fb783cb0abe2b5fded9f55e5703015cdf1c9c85b3669087c538dd15a6a86/comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e", size = 6210 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/e6/75/49e5bfe642f71f272236b5b2d2691cf915a7283cc0ceda56357b61daa538/comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3", size = 7180 },
+]
+
[[package]]
name = "continuing-education"
version = "0.1.0"
@@ -132,7 +306,9 @@ dependencies = [
[package.dev-dependencies]
dev = [
+ { name = "ipywidgets" },
{ name = "jupytext" },
+ { name = "notebook" },
]
[package.metadata]
@@ -156,7 +332,11 @@ requires-dist = [
]
[package.metadata.requires-dev]
-dev = [{ name = "jupytext", specifier = ">=1.16.7" }]
+dev = [
+ { name = "ipywidgets", specifier = ">=8.1.5" },
+ { name = "jupytext", specifier = ">=1.16.7" },
+ { name = "notebook", specifier = ">=7.3.2" },
+]
[[package]]
name = "contourpy"
@@ -218,6 +398,27 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321 },
]
+[[package]]
+name = "debugpy"
+version = "1.8.12"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/68/25/c74e337134edf55c4dfc9af579eccb45af2393c40960e2795a94351e8140/debugpy-1.8.12.tar.gz", hash = "sha256:646530b04f45c830ceae8e491ca1c9320a2d2f0efea3141487c82130aba70dce", size = 1641122 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/af/9f/5b8af282253615296264d4ef62d14a8686f0dcdebb31a669374e22fff0a4/debugpy-1.8.12-cp311-cp311-macosx_14_0_universal2.whl", hash = "sha256:36f4829839ef0afdfdd208bb54f4c3d0eea86106d719811681a8627ae2e53dd5", size = 2174643 },
+ { url = "https://files.pythonhosted.org/packages/ef/31/f9274dcd3b0f9f7d1e60373c3fa4696a585c55acb30729d313bb9d3bcbd1/debugpy-1.8.12-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a28ed481d530e3138553be60991d2d61103ce6da254e51547b79549675f539b7", size = 3133457 },
+ { url = "https://files.pythonhosted.org/packages/ab/ca/6ee59e9892e424477e0c76e3798046f1fd1288040b927319c7a7b0baa484/debugpy-1.8.12-cp311-cp311-win32.whl", hash = "sha256:4ad9a94d8f5c9b954e0e3b137cc64ef3f579d0df3c3698fe9c3734ee397e4abb", size = 5106220 },
+ { url = "https://files.pythonhosted.org/packages/d5/1a/8ab508ab05ede8a4eae3b139bbc06ea3ca6234f9e8c02713a044f253be5e/debugpy-1.8.12-cp311-cp311-win_amd64.whl", hash = "sha256:4703575b78dd697b294f8c65588dc86874ed787b7348c65da70cfc885efdf1e1", size = 5130481 },
+ { url = "https://files.pythonhosted.org/packages/ba/e6/0f876ecfe5831ebe4762b19214364753c8bc2b357d28c5d739a1e88325c7/debugpy-1.8.12-cp312-cp312-macosx_14_0_universal2.whl", hash = "sha256:7e94b643b19e8feb5215fa508aee531387494bf668b2eca27fa769ea11d9f498", size = 2500846 },
+ { url = "https://files.pythonhosted.org/packages/19/64/33f41653a701f3cd2cbff8b41ebaad59885b3428b5afd0d93d16012ecf17/debugpy-1.8.12-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:086b32e233e89a2740c1615c2f775c34ae951508b28b308681dbbb87bba97d06", size = 4222181 },
+ { url = "https://files.pythonhosted.org/packages/32/a6/02646cfe50bfacc9b71321c47dc19a46e35f4e0aceea227b6d205e900e34/debugpy-1.8.12-cp312-cp312-win32.whl", hash = "sha256:2ae5df899732a6051b49ea2632a9ea67f929604fd2b036613a9f12bc3163b92d", size = 5227017 },
+ { url = "https://files.pythonhosted.org/packages/da/a6/10056431b5c47103474312cf4a2ec1001f73e0b63b1216706d5fef2531eb/debugpy-1.8.12-cp312-cp312-win_amd64.whl", hash = "sha256:39dfbb6fa09f12fae32639e3286112fc35ae976114f1f3d37375f3130a820969", size = 5267555 },
+ { url = "https://files.pythonhosted.org/packages/cf/4d/7c3896619a8791effd5d8c31f0834471fc8f8fb3047ec4f5fc69dd1393dd/debugpy-1.8.12-cp313-cp313-macosx_14_0_universal2.whl", hash = "sha256:696d8ae4dff4cbd06bf6b10d671e088b66669f110c7c4e18a44c43cf75ce966f", size = 2485246 },
+ { url = "https://files.pythonhosted.org/packages/99/46/bc6dcfd7eb8cc969a5716d858e32485eb40c72c6a8dc88d1e3a4d5e95813/debugpy-1.8.12-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:898fba72b81a654e74412a67c7e0a81e89723cfe2a3ea6fcd3feaa3395138ca9", size = 4218616 },
+ { url = "https://files.pythonhosted.org/packages/03/dd/d7fcdf0381a9b8094da1f6a1c9f19fed493a4f8576a2682349b3a8b20ec7/debugpy-1.8.12-cp313-cp313-win32.whl", hash = "sha256:22a11c493c70413a01ed03f01c3c3a2fc4478fc6ee186e340487b2edcd6f4180", size = 5226540 },
+ { url = "https://files.pythonhosted.org/packages/25/bd/ecb98f5b5fc7ea0bfbb3c355bc1dd57c198a28780beadd1e19915bf7b4d9/debugpy-1.8.12-cp313-cp313-win_amd64.whl", hash = "sha256:fdb3c6d342825ea10b90e43d7f20f01535a72b3a1997850c0c3cefa5c27a4a2c", size = 5267134 },
+ { url = "https://files.pythonhosted.org/packages/38/c4/5120ad36405c3008f451f94b8f92ef1805b1e516f6ff870f331ccb3c4cc0/debugpy-1.8.12-py2.py3-none-any.whl", hash = "sha256:274b6a2040349b5c9864e475284bce5bb062e63dce368a394b8cc865ae3b00c6", size = 5229490 },
+]
+
[[package]]
name = "decorator"
version = "5.1.1"
@@ -227,6 +428,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/d5/50/83c593b07763e1161326b3b8c6686f0f4b0f24d5526546bee538c89837d6/decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186", size = 9073 },
]
+[[package]]
+name = "defusedxml"
+version = "0.7.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0f/d5/c66da9b79e5bdb124974bfe172b4daf3c984ebd9c2a06e2b8a4dc7331c72/defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69", size = 75520 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604 },
+]
+
[[package]]
name = "executing"
version = "2.2.0"
@@ -296,6 +506,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/bf/ff/44934a031ce5a39125415eb405b9efb76fe7f9586b75291d66ae5cbfc4e6/fonttools-4.56.0-py3-none-any.whl", hash = "sha256:1088182f68c303b50ca4dc0c82d42083d176cba37af1937e1a976a31149d4d14", size = 1089800 },
]
+[[package]]
+name = "fqdn"
+version = "1.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/30/3e/a80a8c077fd798951169626cde3e239adeba7dab75deb3555716415bd9b0/fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f", size = 6015 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/cf/58/8acf1b3e91c58313ce5cb67df61001fc9dcd21be4fadb76c1a2d540e09ed/fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014", size = 9121 },
+]
+
[[package]]
name = "fsspec"
version = "2025.2.0"
@@ -381,6 +600,43 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/a8/4d/3cbfd81ed84db450dbe73a89afcd8bc405273918415649ac6683356afe92/gymnasium-0.29.1-py3-none-any.whl", hash = "sha256:61c3384b5575985bb7f85e43213bcb40f36fcdff388cae6bc229304c71f2843e", size = 953939 },
]
+[[package]]
+name = "h11"
+version = "0.14.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f5/38/3af3d3633a34a3316095b39c8e8fb4853a28a536e55d347bd8d8e9a14b03/h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d", size = 100418 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/95/04/ff642e65ad6b90db43e668d70ffb6736436c7ce41fcc549f4e9472234127/h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761", size = 58259 },
+]
+
+[[package]]
+name = "httpcore"
+version = "1.0.7"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "certifi" },
+ { name = "h11" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/6a/41/d7d0a89eb493922c37d343b607bc1b5da7f5be7e383740b4753ad8943e90/httpcore-1.0.7.tar.gz", hash = "sha256:8551cb62a169ec7162ac7be8d4817d561f60e08eaa485234898414bb5a8a0b4c", size = 85196 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/87/f5/72347bc88306acb359581ac4d52f23c0ef445b57157adedb9aee0cd689d2/httpcore-1.0.7-py3-none-any.whl", hash = "sha256:a3fff8f43dc260d5bd363d9f9cf1830fa3a458b332856f34282de498ed420edd", size = 78551 },
+]
+
+[[package]]
+name = "httpx"
+version = "0.28.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "anyio" },
+ { name = "certifi" },
+ { name = "httpcore" },
+ { name = "idna" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517 },
+]
+
[[package]]
name = "huggingface-hub"
version = "0.21.4"
@@ -417,6 +673,30 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374", size = 5892 },
]
+[[package]]
+name = "ipykernel"
+version = "6.29.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "appnope", marker = "platform_system == 'Darwin'" },
+ { name = "comm" },
+ { name = "debugpy" },
+ { name = "ipython" },
+ { name = "jupyter-client" },
+ { name = "jupyter-core" },
+ { name = "matplotlib-inline" },
+ { name = "nest-asyncio" },
+ { name = "packaging" },
+ { name = "psutil" },
+ { name = "pyzmq" },
+ { name = "tornado" },
+ { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e9/5c/67594cb0c7055dc50814b21731c22a601101ea3b1b50a9a1b090e11f5d0f/ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215", size = 163367 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/94/5c/368ae6c01c7628438358e6d337c19b05425727fbb221d2a3c4303c372f42/ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5", size = 117173 },
+]
+
[[package]]
name = "ipytest"
version = "0.14.2"
@@ -452,6 +732,34 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/e7/e1/f4474a7ecdb7745a820f6f6039dc43c66add40f1bcc66485607d93571af6/ipython-8.32.0-py3-none-any.whl", hash = "sha256:cae85b0c61eff1fc48b0a8002de5958b6528fa9c8defb1894da63f42613708aa", size = 825524 },
]
+[[package]]
+name = "ipywidgets"
+version = "8.1.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "comm" },
+ { name = "ipython" },
+ { name = "jupyterlab-widgets" },
+ { name = "traitlets" },
+ { name = "widgetsnbextension" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c7/4c/dab2a281b07596a5fc220d49827fe6c794c66f1493d7a74f1df0640f2cc5/ipywidgets-8.1.5.tar.gz", hash = "sha256:870e43b1a35656a80c18c9503bbf2d16802db1cb487eec6fab27d683381dde17", size = 116723 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/22/2d/9c0b76f2f9cc0ebede1b9371b6f317243028ed60b90705863d493bae622e/ipywidgets-8.1.5-py3-none-any.whl", hash = "sha256:3290f526f87ae6e77655555baba4f36681c555b8bdbbff430b70e52c34c86245", size = 139767 },
+]
+
+[[package]]
+name = "isoduration"
+version = "20.11.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "arrow" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7c/1a/3c8edc664e06e6bd06cce40c6b22da5f1429aa4224d0c590f3be21c91ead/isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9", size = 11649 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/7b/55/e5326141505c5d5e34c5e0935d2908a74e4561eca44108fbfb9c13d2911a/isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042", size = 11321 },
+]
+
[[package]]
name = "jedi"
version = "0.19.2"
@@ -476,6 +784,24 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/bd/0f/2ba5fbcd631e3e88689309dbe978c5769e883e4b84ebfe7da30b43275c5a/jinja2-3.1.5-py3-none-any.whl", hash = "sha256:aba0f4dc9ed8013c424088f68a5c226f7d6097ed89b246d7749c2ec4175c6adb", size = 134596 },
]
+[[package]]
+name = "json5"
+version = "0.10.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/85/3d/bbe62f3d0c05a689c711cff57b2e3ac3d3e526380adb7c781989f075115c/json5-0.10.0.tar.gz", hash = "sha256:e66941c8f0a02026943c52c2eb34ebeb2a6f819a0be05920a6f5243cd30fd559", size = 48202 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/aa/42/797895b952b682c3dafe23b1834507ee7f02f4d6299b65aaa61425763278/json5-0.10.0-py3-none-any.whl", hash = "sha256:19b23410220a7271e8377f81ba8aacba2fdd56947fbb137ee5977cbe1f5e8dfa", size = 34049 },
+]
+
+[[package]]
+name = "jsonpointer"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6a/0a/eebeb1fa92507ea94016a2a790b93c2ae41a7e18778f85471dc54475ed25/jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef", size = 9114 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/71/92/5e77f98553e9e75130c78900d000368476aed74276eb8ae8796f65f00918/jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942", size = 7595 },
+]
+
[[package]]
name = "jsonschema"
version = "4.23.0"
@@ -491,6 +817,18 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/69/4a/4f9dbeb84e8850557c02365a0eee0649abe5eb1d84af92a25731c6c0f922/jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566", size = 88462 },
]
+[package.optional-dependencies]
+format-nongpl = [
+ { name = "fqdn" },
+ { name = "idna" },
+ { name = "isoduration" },
+ { name = "jsonpointer" },
+ { name = "rfc3339-validator" },
+ { name = "rfc3986-validator" },
+ { name = "uri-template" },
+ { name = "webcolors" },
+]
+
[[package]]
name = "jsonschema-specifications"
version = "2024.10.1"
@@ -503,6 +841,22 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/d1/0f/8910b19ac0670a0f80ce1008e5e751c4a57e14d2c4c13a482aa6079fa9d6/jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf", size = 18459 },
]
+[[package]]
+name = "jupyter-client"
+version = "8.6.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "jupyter-core" },
+ { name = "python-dateutil" },
+ { name = "pyzmq" },
+ { name = "tornado" },
+ { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/71/22/bf9f12fdaeae18019a468b68952a60fe6dbab5d67cd2a103cac7659b41ca/jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419", size = 342019 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/11/85/b0394e0b6fcccd2c1eeefc230978a6f8cb0c5df1e4cd3e7625735a0d7d1e/jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f", size = 106105 },
+]
+
[[package]]
name = "jupyter-core"
version = "5.7.2"
@@ -517,6 +871,140 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/c9/fb/108ecd1fe961941959ad0ee4e12ee7b8b1477247f30b1fdfd83ceaf017f0/jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409", size = 28965 },
]
+[[package]]
+name = "jupyter-events"
+version = "0.12.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "jsonschema", extra = ["format-nongpl"] },
+ { name = "packaging" },
+ { name = "python-json-logger" },
+ { name = "pyyaml" },
+ { name = "referencing" },
+ { name = "rfc3339-validator" },
+ { name = "rfc3986-validator" },
+ { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9d/c3/306d090461e4cf3cd91eceaff84bede12a8e52cd821c2d20c9a4fd728385/jupyter_events-0.12.0.tar.gz", hash = "sha256:fc3fce98865f6784c9cd0a56a20644fc6098f21c8c33834a8d9fe383c17e554b", size = 62196 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/e2/48/577993f1f99c552f18a0428731a755e06171f9902fa118c379eb7c04ea22/jupyter_events-0.12.0-py3-none-any.whl", hash = "sha256:6464b2fa5ad10451c3d35fabc75eab39556ae1e2853ad0c0cc31b656731a97fb", size = 19430 },
+]
+
+[[package]]
+name = "jupyter-lsp"
+version = "2.2.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "jupyter-server" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/85/b4/3200b0b09c12bc3b72d943d923323c398eff382d1dcc7c0dbc8b74630e40/jupyter-lsp-2.2.5.tar.gz", hash = "sha256:793147a05ad446f809fd53ef1cd19a9f5256fd0a2d6b7ce943a982cb4f545001", size = 48741 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/07/e0/7bd7cff65594fd9936e2f9385701e44574fc7d721331ff676ce440b14100/jupyter_lsp-2.2.5-py3-none-any.whl", hash = "sha256:45fbddbd505f3fbfb0b6cb2f1bc5e15e83ab7c79cd6e89416b248cb3c00c11da", size = 69146 },
+]
+
+[[package]]
+name = "jupyter-server"
+version = "2.15.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "anyio" },
+ { name = "argon2-cffi" },
+ { name = "jinja2" },
+ { name = "jupyter-client" },
+ { name = "jupyter-core" },
+ { name = "jupyter-events" },
+ { name = "jupyter-server-terminals" },
+ { name = "nbconvert" },
+ { name = "nbformat" },
+ { name = "overrides" },
+ { name = "packaging" },
+ { name = "prometheus-client" },
+ { name = "pywinpty", marker = "os_name == 'nt'" },
+ { name = "pyzmq" },
+ { name = "send2trash" },
+ { name = "terminado" },
+ { name = "tornado" },
+ { name = "traitlets" },
+ { name = "websocket-client" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/61/8c/df09d4ab646141f130f9977b32b206ba8615d1969b2eba6a2e84b7f89137/jupyter_server-2.15.0.tar.gz", hash = "sha256:9d446b8697b4f7337a1b7cdcac40778babdd93ba614b6d68ab1c0c918f1c4084", size = 725227 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/e2/a2/89eeaf0bb954a123a909859fa507fa86f96eb61b62dc30667b60dbd5fdaf/jupyter_server-2.15.0-py3-none-any.whl", hash = "sha256:872d989becf83517012ee669f09604aa4a28097c0bd90b2f424310156c2cdae3", size = 385826 },
+]
+
+[[package]]
+name = "jupyter-server-terminals"
+version = "0.5.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "pywinpty", marker = "os_name == 'nt'" },
+ { name = "terminado" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fc/d5/562469734f476159e99a55426d697cbf8e7eb5efe89fb0e0b4f83a3d3459/jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269", size = 31430 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/07/2d/2b32cdbe8d2a602f697a649798554e4f072115438e92249624e532e8aca6/jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa", size = 13656 },
+]
+
+[[package]]
+name = "jupyterlab"
+version = "4.3.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "async-lru" },
+ { name = "httpx" },
+ { name = "ipykernel" },
+ { name = "jinja2" },
+ { name = "jupyter-core" },
+ { name = "jupyter-lsp" },
+ { name = "jupyter-server" },
+ { name = "jupyterlab-server" },
+ { name = "notebook-shim" },
+ { name = "packaging" },
+ { name = "setuptools" },
+ { name = "tornado" },
+ { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/19/17/6f3d73c3e54b71bbaf03edcc4a54b0aa6328e0a134755f297ea87d425711/jupyterlab-4.3.5.tar.gz", hash = "sha256:c779bf72ced007d7d29d5bcef128e7fdda96ea69299e19b04a43635a7d641f9d", size = 21800023 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/73/6f/94d4c879b3e2b7b9bca1913ea6fbbef180f8b1ed065b46ade40d651ec54d/jupyterlab-4.3.5-py3-none-any.whl", hash = "sha256:571bbdee20e4c5321ab5195bc41cf92a75a5cff886be5e57ce78dfa37a5e9fdb", size = 11666944 },
+]
+
+[[package]]
+name = "jupyterlab-pygments"
+version = "0.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/90/51/9187be60d989df97f5f0aba133fa54e7300f17616e065d1ada7d7646b6d6/jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d", size = 512900 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/b1/dd/ead9d8ea85bf202d90cc513b533f9c363121c7792674f78e0d8a854b63b4/jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780", size = 15884 },
+]
+
+[[package]]
+name = "jupyterlab-server"
+version = "2.27.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "babel" },
+ { name = "jinja2" },
+ { name = "json5" },
+ { name = "jsonschema" },
+ { name = "jupyter-server" },
+ { name = "packaging" },
+ { name = "requests" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0a/c9/a883ce65eb27905ce77ace410d83587c82ea64dc85a48d1f7ed52bcfa68d/jupyterlab_server-2.27.3.tar.gz", hash = "sha256:eb36caca59e74471988f0ae25c77945610b887f777255aa21f8065def9e51ed4", size = 76173 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/54/09/2032e7d15c544a0e3cd831c51d77a8ca57f7555b2e1b2922142eddb02a84/jupyterlab_server-2.27.3-py3-none-any.whl", hash = "sha256:e697488f66c3db49df675158a77b3b017520d772c6e1548c7d9bcc5df7944ee4", size = 59700 },
+]
+
+[[package]]
+name = "jupyterlab-widgets"
+version = "3.0.13"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/59/73/fa26bbb747a9ea4fca6b01453aa22990d52ab62dd61384f1ac0dc9d4e7ba/jupyterlab_widgets-3.0.13.tar.gz", hash = "sha256:a2966d385328c1942b683a8cd96b89b8dd82c8b8f81dda902bb2bc06d46f5bed", size = 203556 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/a9/93/858e87edc634d628e5d752ba944c2833133a28fa87bb093e6832ced36a3e/jupyterlab_widgets-3.0.13-py3-none-any.whl", hash = "sha256:e3cda2c233ce144192f1e29914ad522b2f4c40e77214b0cc97377ca3d323db54", size = 214392 },
+]
+
[[package]]
name = "jupytext"
version = "1.16.7"
@@ -757,6 +1245,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/c6/6d/aa5ef58c2c45a8de8ac37599179df26b6fd47781d6b2614d2116b8bfca5c/MinAtar-1.0.15-py3-none-any.whl", hash = "sha256:f40450e133c552b97222702bb7b1c5254b47758390f4bd25e411399af0b21d42", size = 16606 },
]
+[[package]]
+name = "mistune"
+version = "3.1.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/80/f7/f6d06304c61c2a73213c0a4815280f70d985429cda26272f490e42119c1a/mistune-3.1.2.tar.gz", hash = "sha256:733bf018ba007e8b5f2d3a9eb624034f6ee26c4ea769a98ec533ee111d504dff", size = 94613 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/12/92/30b4e54c4d7c48c06db61595cffbbf4f19588ea177896f9b78f0fbe021fd/mistune-3.1.2-py3-none-any.whl", hash = "sha256:4b47731332315cdca99e0ded46fc0004001c1299ff773dfb48fbe1fd226de319", size = 53696 },
+]
+
[[package]]
name = "mpmath"
version = "1.3.0"
@@ -766,6 +1263,46 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198 },
]
+[[package]]
+name = "nbclient"
+version = "0.10.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "jupyter-client" },
+ { name = "jupyter-core" },
+ { name = "nbformat" },
+ { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/87/66/7ffd18d58eae90d5721f9f39212327695b749e23ad44b3881744eaf4d9e8/nbclient-0.10.2.tar.gz", hash = "sha256:90b7fc6b810630db87a6d0c2250b1f0ab4cf4d3c27a299b0cde78a4ed3fd9193", size = 62424 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/34/6d/e7fa07f03a4a7b221d94b4d586edb754a9b0dc3c9e2c93353e9fa4e0d117/nbclient-0.10.2-py3-none-any.whl", hash = "sha256:4ffee11e788b4a27fabeb7955547e4318a5298f34342a4bfd01f2e1faaeadc3d", size = 25434 },
+]
+
+[[package]]
+name = "nbconvert"
+version = "7.16.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "beautifulsoup4" },
+ { name = "bleach", extra = ["css"] },
+ { name = "defusedxml" },
+ { name = "jinja2" },
+ { name = "jupyter-core" },
+ { name = "jupyterlab-pygments" },
+ { name = "markupsafe" },
+ { name = "mistune" },
+ { name = "nbclient" },
+ { name = "nbformat" },
+ { name = "packaging" },
+ { name = "pandocfilters" },
+ { name = "pygments" },
+ { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a3/59/f28e15fc47ffb73af68a8d9b47367a8630d76e97ae85ad18271b9db96fdf/nbconvert-7.16.6.tar.gz", hash = "sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582", size = 857715 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/cc/9a/cd673b2f773a12c992f41309ef81b99da1690426bd2f96957a7ade0d3ed7/nbconvert-7.16.6-py3-none-any.whl", hash = "sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b", size = 258525 },
+]
+
[[package]]
name = "nbformat"
version = "5.10.4"
@@ -781,6 +1318,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b", size = 78454 },
]
+[[package]]
+name = "nest-asyncio"
+version = "1.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195 },
+]
+
[[package]]
name = "networkx"
version = "3.4.2"
@@ -790,6 +1336,34 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/b9/54/dd730b32ea14ea797530a4479b2ed46a6fb250f682a9cfb997e968bf0261/networkx-3.4.2-py3-none-any.whl", hash = "sha256:df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f", size = 1723263 },
]
+[[package]]
+name = "notebook"
+version = "7.3.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "jupyter-server" },
+ { name = "jupyterlab" },
+ { name = "jupyterlab-server" },
+ { name = "notebook-shim" },
+ { name = "tornado" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ea/04/ac488379d5afef43402b3fb4be2857db1a09804fecf98b9b714c741b225b/notebook-7.3.2.tar.gz", hash = "sha256:705e83a1785f45b383bf3ee13cb76680b92d24f56fb0c7d2136fe1d850cd3ca8", size = 12781804 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/22/9b/76e50ee18f183ea5fe1784a9eeaa50f2c71802e4740d6e959592b0993298/notebook-7.3.2-py3-none-any.whl", hash = "sha256:e5f85fc59b69d3618d73cf27544418193ff8e8058d5bf61d315ce4f473556288", size = 13163630 },
+]
+
+[[package]]
+name = "notebook-shim"
+version = "0.2.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "jupyter-server" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/54/d2/92fa3243712b9a3e8bafaf60aac366da1cada3639ca767ff4b5b3654ec28/notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb", size = 13167 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/f9/33/bd5b9137445ea4b680023eb0469b2bb969d61303dedb2aac6560ff3d14a1/notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef", size = 13307 },
+]
+
[[package]]
name = "numpy"
version = "1.26.4"
@@ -923,6 +1497,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/da/d3/8057f0587683ed2fcd4dbfbdfdfa807b9160b809976099d36b8f60d08f03/nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5", size = 99138 },
]
+[[package]]
+name = "overrides"
+version = "7.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/36/86/b585f53236dec60aba864e050778b25045f857e17f6e5ea0ae95fe80edd2/overrides-7.7.0.tar.gz", hash = "sha256:55158fa3d93b98cc75299b1e67078ad9003ca27945c76162c1c0766d6f91820a", size = 22812 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/2c/ab/fc8290c6a4c722e5514d80f62b2dc4c4df1a68a41d1364e625c35990fcf3/overrides-7.7.0-py3-none-any.whl", hash = "sha256:c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49", size = 17832 },
+]
+
[[package]]
name = "packaging"
version = "24.2"
@@ -973,6 +1556,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/ab/5f/b38085618b950b79d2d9164a711c52b10aefc0ae6833b96f626b7021b2ed/pandas-2.2.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:ad5b65698ab28ed8d7f18790a0dc58005c7629f227be9ecc1072aa74c0c1d43a", size = 13098436 },
]
+[[package]]
+name = "pandocfilters"
+version = "1.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/70/6f/3dd4940bbe001c06a65f88e36bad298bc7a0de5036115639926b0c5c0458/pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e", size = 8454 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/ef/af/4fbc8cab944db5d21b7e2a5b8e9211a03a79852b1157e2c102fcc61ac440/pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc", size = 8663 },
+]
+
[[package]]
name = "parso"
version = "0.8.4"
@@ -1064,6 +1656,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669", size = 20556 },
]
+[[package]]
+name = "prometheus-client"
+version = "0.21.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/62/14/7d0f567991f3a9af8d1cd4f619040c93b68f09a02b6d0b6ab1b2d1ded5fe/prometheus_client-0.21.1.tar.gz", hash = "sha256:252505a722ac04b0456be05c05f75f45d760c2911ffc45f2a06bcaed9f3ae3fb", size = 78551 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/ff/c2/ab7d37426c179ceb9aeb109a85cda8948bb269b7561a0be870cc656eefe4/prometheus_client-0.21.1-py3-none-any.whl", hash = "sha256:594b45c410d6f4f8888940fe80b5cc2521b305a1fafe1c58609ef715a001f301", size = 54682 },
+]
+
[[package]]
name = "prompt-toolkit"
version = "3.0.50"
@@ -1076,6 +1677,21 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/e4/ea/d836f008d33151c7a1f62caf3d8dd782e4d15f6a43897f64480c2b8de2ad/prompt_toolkit-3.0.50-py3-none-any.whl", hash = "sha256:9b6427eb19e479d98acff65196a307c555eb567989e6d88ebbb1b509d9779198", size = 387816 },
]
+[[package]]
+name = "psutil"
+version = "7.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2a/80/336820c1ad9286a4ded7e845b2eccfcb27851ab8ac6abece774a6ff4d3de/psutil-7.0.0.tar.gz", hash = "sha256:7be9c3eba38beccb6495ea33afd982a44074b78f28c434a1f51cc07fd315c456", size = 497003 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/ed/e6/2d26234410f8b8abdbf891c9da62bee396583f713fb9f3325a4760875d22/psutil-7.0.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:101d71dc322e3cffd7cea0650b09b3d08b8e7c4109dd6809fe452dfd00e58b25", size = 238051 },
+ { url = "https://files.pythonhosted.org/packages/04/8b/30f930733afe425e3cbfc0e1468a30a18942350c1a8816acfade80c005c4/psutil-7.0.0-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:39db632f6bb862eeccf56660871433e111b6ea58f2caea825571951d4b6aa3da", size = 239535 },
+ { url = "https://files.pythonhosted.org/packages/2a/ed/d362e84620dd22876b55389248e522338ed1bf134a5edd3b8231d7207f6d/psutil-7.0.0-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1fcee592b4c6f146991ca55919ea3d1f8926497a713ed7faaf8225e174581e91", size = 275004 },
+ { url = "https://files.pythonhosted.org/packages/bf/b9/b0eb3f3cbcb734d930fdf839431606844a825b23eaf9a6ab371edac8162c/psutil-7.0.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b1388a4f6875d7e2aff5c4ca1cc16c545ed41dd8bb596cefea80111db353a34", size = 277986 },
+ { url = "https://files.pythonhosted.org/packages/eb/a2/709e0fe2f093556c17fbafda93ac032257242cabcc7ff3369e2cb76a97aa/psutil-7.0.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5f098451abc2828f7dc6b58d44b532b22f2088f4999a937557b603ce72b1993", size = 279544 },
+ { url = "https://files.pythonhosted.org/packages/50/e6/eecf58810b9d12e6427369784efe814a1eec0f492084ce8eb8f4d89d6d61/psutil-7.0.0-cp37-abi3-win32.whl", hash = "sha256:ba3fcef7523064a6c9da440fc4d6bd07da93ac726b5733c29027d7dc95b39d99", size = 241053 },
+ { url = "https://files.pythonhosted.org/packages/50/1b/6921afe68c74868b4c9fa424dad3be35b095e16687989ebbb50ce4fceb7c/psutil-7.0.0-cp37-abi3-win_amd64.whl", hash = "sha256:4cf3d4eb1aa9b348dec30105c55cd9b7d4629285735a102beb4441e38db90553", size = 244885 },
+]
+
[[package]]
name = "ptyprocess"
version = "0.7.0"
@@ -1094,6 +1710,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842 },
]
+[[package]]
+name = "pycparser"
+version = "2.22"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1d/b2/31537cf4b1ca988837256c910a668b553fceb8f069bedc4b1c826024b52c/pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6", size = 172736 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/13/a3/a812df4e2dd5696d1f351d58b8fe16a405b234ad2886a0dab9183fb78109/pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc", size = 117552 },
+]
+
[[package]]
name = "pydantic"
version = "2.6.4"
@@ -1212,6 +1837,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892 },
]
+[[package]]
+name = "python-json-logger"
+version = "3.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e3/c4/358cd13daa1d912ef795010897a483ab2f0b41c9ea1b35235a8b2f7d15a7/python_json_logger-3.2.1.tar.gz", hash = "sha256:8eb0554ea17cb75b05d2848bc14fb02fbdbd9d6972120781b974380bfa162008", size = 16287 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/4b/72/2f30cf26664fcfa0bd8ec5ee62ec90c03bd485e4a294d92aabc76c5203a5/python_json_logger-3.2.1-py3-none-any.whl", hash = "sha256:cdc17047eb5374bd311e748b42f99d71223f3b0e186f4206cc5d52aefe85b090", size = 14924 },
+]
+
[[package]]
name = "pytz"
version = "2025.1"
@@ -1237,6 +1871,18 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/26/df/2b63e3e4f2df0224f8aaf6d131f54fe4e8c96400eb9df563e2aae2e1a1f9/pywin32-308-cp313-cp313-win_arm64.whl", hash = "sha256:ef313c46d4c18dfb82a2431e3051ac8f112ccee1a34f29c263c583c568db63cd", size = 7974986 },
]
+[[package]]
+name = "pywinpty"
+version = "2.0.15"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2d/7c/917f9c4681bb8d34bfbe0b79d36bbcd902651aeab48790df3d30ba0202fb/pywinpty-2.0.15.tar.gz", hash = "sha256:312cf39153a8736c617d45ce8b6ad6cd2107de121df91c455b10ce6bba7a39b2", size = 29017 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/5e/ac/6884dcb7108af66ad53f73ef4dad096e768c9203a6e6ce5e6b0c4a46e238/pywinpty-2.0.15-cp311-cp311-win_amd64.whl", hash = "sha256:9a6bcec2df2707aaa9d08b86071970ee32c5026e10bcc3cc5f6f391d85baf7ca", size = 1405249 },
+ { url = "https://files.pythonhosted.org/packages/88/e5/9714def18c3a411809771a3fbcec70bffa764b9675afb00048a620fca604/pywinpty-2.0.15-cp312-cp312-win_amd64.whl", hash = "sha256:83a8f20b430bbc5d8957249f875341a60219a4e971580f2ba694fbfb54a45ebc", size = 1405243 },
+ { url = "https://files.pythonhosted.org/packages/fb/16/2ab7b3b7f55f3c6929e5f629e1a68362981e4e5fed592a2ed1cb4b4914a5/pywinpty-2.0.15-cp313-cp313-win_amd64.whl", hash = "sha256:ab5920877dd632c124b4ed17bc6dd6ef3b9f86cd492b963ffdb1a67b85b0f408", size = 1405020 },
+ { url = "https://files.pythonhosted.org/packages/7c/16/edef3515dd2030db2795dbfbe392232c7a0f3dc41b98e92b38b42ba497c7/pywinpty-2.0.15-cp313-cp313t-win_amd64.whl", hash = "sha256:a4560ad8c01e537708d2790dbe7da7d986791de805d89dd0d3697ca59e9e4901", size = 1404151 },
+]
+
[[package]]
name = "pyyaml"
version = "6.0.2"
@@ -1272,6 +1918,62 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/fa/de/02b54f42487e3d3c6efb3f89428677074ca7bf43aae402517bc7cca949f3/PyYAML-6.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:8388ee1976c416731879ac16da0aff3f63b286ffdd57cdeb95f3f2e085687563", size = 156446 },
]
+[[package]]
+name = "pyzmq"
+version = "26.2.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "cffi", marker = "implementation_name == 'pypy'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5a/e3/8d0382cb59feb111c252b54e8728257416a38ffcb2243c4e4775a3c990fe/pyzmq-26.2.1.tar.gz", hash = "sha256:17d72a74e5e9ff3829deb72897a175333d3ef5b5413948cae3cf7ebf0b02ecca", size = 278433 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/b9/03/5ecc46a6ed5971299f5c03e016ca637802d8660e44392bea774fb7797405/pyzmq-26.2.1-cp311-cp311-macosx_10_15_universal2.whl", hash = "sha256:c059883840e634a21c5b31d9b9a0e2b48f991b94d60a811092bc37992715146a", size = 1346032 },
+ { url = "https://files.pythonhosted.org/packages/40/51/48fec8f990ee644f461ff14c8fe5caa341b0b9b3a0ad7544f8ef17d6f528/pyzmq-26.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:ed038a921df836d2f538e509a59cb638df3e70ca0fcd70d0bf389dfcdf784d2a", size = 943324 },
+ { url = "https://files.pythonhosted.org/packages/c1/f4/f322b389727c687845e38470b48d7a43c18a83f26d4d5084603c6c3f79ca/pyzmq-26.2.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9027a7fcf690f1a3635dc9e55e38a0d6602dbbc0548935d08d46d2e7ec91f454", size = 678418 },
+ { url = "https://files.pythonhosted.org/packages/a8/df/2834e3202533bd05032d83e02db7ac09fa1be853bbef59974f2b2e3a8557/pyzmq-26.2.1-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6d75fcb00a1537f8b0c0bb05322bc7e35966148ffc3e0362f0369e44a4a1de99", size = 915466 },
+ { url = "https://files.pythonhosted.org/packages/b5/e2/45c0f6e122b562cb8c6c45c0dcac1160a4e2207385ef9b13463e74f93031/pyzmq-26.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f0019cc804ac667fb8c8eaecdb66e6d4a68acf2e155d5c7d6381a5645bd93ae4", size = 873347 },
+ { url = "https://files.pythonhosted.org/packages/de/b9/3e0fbddf8b87454e914501d368171466a12550c70355b3844115947d68ea/pyzmq-26.2.1-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:f19dae58b616ac56b96f2e2290f2d18730a898a171f447f491cc059b073ca1fa", size = 874545 },
+ { url = "https://files.pythonhosted.org/packages/1f/1c/1ee41d6e10b2127263b1994bc53b9e74ece015b0d2c0a30e0afaf69b78b2/pyzmq-26.2.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:f5eeeb82feec1fc5cbafa5ee9022e87ffdb3a8c48afa035b356fcd20fc7f533f", size = 1208630 },
+ { url = "https://files.pythonhosted.org/packages/3d/a9/50228465c625851a06aeee97c74f253631f509213f979166e83796299c60/pyzmq-26.2.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:000760e374d6f9d1a3478a42ed0c98604de68c9e94507e5452951e598ebecfba", size = 1519568 },
+ { url = "https://files.pythonhosted.org/packages/c6/f2/6360b619e69da78863c2108beb5196ae8b955fe1e161c0b886b95dc6b1ac/pyzmq-26.2.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:817fcd3344d2a0b28622722b98500ae9c8bfee0f825b8450932ff19c0b15bebd", size = 1419677 },
+ { url = "https://files.pythonhosted.org/packages/da/d5/f179da989168f5dfd1be8103ef508ade1d38a8078dda4f10ebae3131a490/pyzmq-26.2.1-cp311-cp311-win32.whl", hash = "sha256:88812b3b257f80444a986b3596e5ea5c4d4ed4276d2b85c153a6fbc5ca457ae7", size = 582682 },
+ { url = "https://files.pythonhosted.org/packages/60/50/e5b2e9de3ffab73ff92bee736216cf209381081fa6ab6ba96427777d98b1/pyzmq-26.2.1-cp311-cp311-win_amd64.whl", hash = "sha256:ef29630fde6022471d287c15c0a2484aba188adbfb978702624ba7a54ddfa6c1", size = 648128 },
+ { url = "https://files.pythonhosted.org/packages/d9/fe/7bb93476dd8405b0fc9cab1fd921a08bd22d5e3016aa6daea1a78d54129b/pyzmq-26.2.1-cp311-cp311-win_arm64.whl", hash = "sha256:f32718ee37c07932cc336096dc7403525301fd626349b6eff8470fe0f996d8d7", size = 562465 },
+ { url = "https://files.pythonhosted.org/packages/9c/b9/260a74786f162c7f521f5f891584a51d5a42fd15f5dcaa5c9226b2865fcc/pyzmq-26.2.1-cp312-cp312-macosx_10_15_universal2.whl", hash = "sha256:a6549ecb0041dafa55b5932dcbb6c68293e0bd5980b5b99f5ebb05f9a3b8a8f3", size = 1348495 },
+ { url = "https://files.pythonhosted.org/packages/bf/73/8a0757e4b68f5a8ccb90ddadbb76c6a5f880266cdb18be38c99bcdc17aaa/pyzmq-26.2.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:0250c94561f388db51fd0213cdccbd0b9ef50fd3c57ce1ac937bf3034d92d72e", size = 945035 },
+ { url = "https://files.pythonhosted.org/packages/cf/de/f02ec973cd33155bb772bae33ace774acc7cc71b87b25c4829068bec35de/pyzmq-26.2.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:36ee4297d9e4b34b5dc1dd7ab5d5ea2cbba8511517ef44104d2915a917a56dc8", size = 671213 },
+ { url = "https://files.pythonhosted.org/packages/d1/80/8fc583085f85ac91682744efc916888dd9f11f9f75a31aef1b78a5486c6c/pyzmq-26.2.1-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c2a9cb17fd83b7a3a3009901aca828feaf20aa2451a8a487b035455a86549c09", size = 908750 },
+ { url = "https://files.pythonhosted.org/packages/c3/25/0b4824596f261a3cc512ab152448b383047ff5f143a6906a36876415981c/pyzmq-26.2.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:786dd8a81b969c2081b31b17b326d3a499ddd1856e06d6d79ad41011a25148da", size = 865416 },
+ { url = "https://files.pythonhosted.org/packages/a1/d1/6fda77a034d02034367b040973fd3861d945a5347e607bd2e98c99f20599/pyzmq-26.2.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:2d88ba221a07fc2c5581565f1d0fe8038c15711ae79b80d9462e080a1ac30435", size = 865922 },
+ { url = "https://files.pythonhosted.org/packages/ad/81/48f7fd8a71c427412e739ce576fc1ee14f3dc34527ca9b0076e471676183/pyzmq-26.2.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:1c84c1297ff9f1cd2440da4d57237cb74be21fdfe7d01a10810acba04e79371a", size = 1201526 },
+ { url = "https://files.pythonhosted.org/packages/c7/d8/818f15c6ef36b5450e435cbb0d3a51599fc884a5d2b27b46b9c00af68ef1/pyzmq-26.2.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:46d4ebafc27081a7f73a0f151d0c38d4291656aa134344ec1f3d0199ebfbb6d4", size = 1512808 },
+ { url = "https://files.pythonhosted.org/packages/d9/c4/b3edb7d0ae82ad6fb1a8cdb191a4113c427a01e85139906f3b655b07f4f8/pyzmq-26.2.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:91e2bfb8e9a29f709d51b208dd5f441dc98eb412c8fe75c24ea464734ccdb48e", size = 1411836 },
+ { url = "https://files.pythonhosted.org/packages/69/1c/151e3d42048f02cc5cd6dfc241d9d36b38375b4dee2e728acb5c353a6d52/pyzmq-26.2.1-cp312-cp312-win32.whl", hash = "sha256:4a98898fdce380c51cc3e38ebc9aa33ae1e078193f4dc641c047f88b8c690c9a", size = 581378 },
+ { url = "https://files.pythonhosted.org/packages/b6/b9/d59a7462848aaab7277fddb253ae134a570520115d80afa85e952287e6bc/pyzmq-26.2.1-cp312-cp312-win_amd64.whl", hash = "sha256:a0741edbd0adfe5f30bba6c5223b78c131b5aa4a00a223d631e5ef36e26e6d13", size = 643737 },
+ { url = "https://files.pythonhosted.org/packages/55/09/f37e707937cce328944c1d57e5e50ab905011d35252a0745c4f7e5822a76/pyzmq-26.2.1-cp312-cp312-win_arm64.whl", hash = "sha256:e5e33b1491555843ba98d5209439500556ef55b6ab635f3a01148545498355e5", size = 558303 },
+ { url = "https://files.pythonhosted.org/packages/4f/2e/fa7a91ce349975971d6aa925b4c7e1a05abaae99b97ade5ace758160c43d/pyzmq-26.2.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:099b56ef464bc355b14381f13355542e452619abb4c1e57a534b15a106bf8e23", size = 942331 },
+ { url = "https://files.pythonhosted.org/packages/64/2b/1f10b34b6dc7ff4b40f668ea25ba9b8093ce61d874c784b90229b367707b/pyzmq-26.2.1-cp313-cp313-macosx_10_15_universal2.whl", hash = "sha256:651726f37fcbce9f8dd2a6dab0f024807929780621890a4dc0c75432636871be", size = 1345831 },
+ { url = "https://files.pythonhosted.org/packages/4c/8d/34884cbd4a8ec050841b5fb58d37af136766a9f95b0b2634c2971deb09da/pyzmq-26.2.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:57dd4d91b38fa4348e237a9388b4423b24ce9c1695bbd4ba5a3eada491e09399", size = 670773 },
+ { url = "https://files.pythonhosted.org/packages/0f/f4/d4becfcf9e416ad2564f18a6653f7c6aa917da08df5c3760edb0baa1c863/pyzmq-26.2.1-cp313-cp313-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d51a7bfe01a48e1064131f3416a5439872c533d756396be2b39e3977b41430f9", size = 908836 },
+ { url = "https://files.pythonhosted.org/packages/07/fa/ab105f1b86b85cb2e821239f1d0900fccd66192a91d97ee04661b5436b4d/pyzmq-26.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c7154d228502e18f30f150b7ce94f0789d6b689f75261b623f0fdc1eec642aab", size = 865369 },
+ { url = "https://files.pythonhosted.org/packages/c9/48/15d5f415504572dd4b92b52db5de7a5befc76bb75340ba9f36f71306a66d/pyzmq-26.2.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:f1f31661a80cc46aba381bed475a9135b213ba23ca7ff6797251af31510920ce", size = 865676 },
+ { url = "https://files.pythonhosted.org/packages/7e/35/2d91bcc7ccbb56043dd4d2c1763f24a8de5f05e06a134f767a7fb38e149c/pyzmq-26.2.1-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:290c96f479504439b6129a94cefd67a174b68ace8a8e3f551b2239a64cfa131a", size = 1201457 },
+ { url = "https://files.pythonhosted.org/packages/6d/bb/aa7c5119307a5762b8dca6c9db73e3ab4bccf32b15d7c4f376271ff72b2b/pyzmq-26.2.1-cp313-cp313-musllinux_1_1_i686.whl", hash = "sha256:f2c307fbe86e18ab3c885b7e01de942145f539165c3360e2af0f094dd440acd9", size = 1513035 },
+ { url = "https://files.pythonhosted.org/packages/4f/4c/527e6650c2fccec7750b783301329c8a8716d59423818afb67282304ce5a/pyzmq-26.2.1-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:b314268e716487bfb86fcd6f84ebbe3e5bec5fac75fdf42bc7d90fdb33f618ad", size = 1411881 },
+ { url = "https://files.pythonhosted.org/packages/89/9f/e4412ea1b3e220acc21777a5edba8885856403d29c6999aaf00a9459eb03/pyzmq-26.2.1-cp313-cp313-win32.whl", hash = "sha256:edb550616f567cd5603b53bb52a5f842c0171b78852e6fc7e392b02c2a1504bb", size = 581354 },
+ { url = "https://files.pythonhosted.org/packages/55/cd/f89dd3e9fc2da0d1619a82c4afb600c86b52bc72d7584953d460bc8d5027/pyzmq-26.2.1-cp313-cp313-win_amd64.whl", hash = "sha256:100a826a029c8ef3d77a1d4c97cbd6e867057b5806a7276f2bac1179f893d3bf", size = 643560 },
+ { url = "https://files.pythonhosted.org/packages/a7/99/5de4f8912860013f1116f818a0047659bc20d71d1bc1d48f874bdc2d7b9c/pyzmq-26.2.1-cp313-cp313-win_arm64.whl", hash = "sha256:6991ee6c43e0480deb1b45d0c7c2bac124a6540cba7db4c36345e8e092da47ce", size = 558037 },
+ { url = "https://files.pythonhosted.org/packages/06/0b/63b6d7a2f07a77dbc9768c6302ae2d7518bed0c6cee515669ca0d8ec743e/pyzmq-26.2.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:25e720dba5b3a3bb2ad0ad5d33440babd1b03438a7a5220511d0c8fa677e102e", size = 938580 },
+ { url = "https://files.pythonhosted.org/packages/85/38/e5e2c3ffa23ea5f95f1c904014385a55902a11a67cd43c10edf61a653467/pyzmq-26.2.1-cp313-cp313t-macosx_10_15_universal2.whl", hash = "sha256:9ec6abfb701437142ce9544bd6a236addaf803a32628d2260eb3dbd9a60e2891", size = 1339670 },
+ { url = "https://files.pythonhosted.org/packages/d2/87/da5519ed7f8b31e4beee8f57311ec02926822fe23a95120877354cd80144/pyzmq-26.2.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2e1eb9d2bfdf5b4e21165b553a81b2c3bd5be06eeddcc4e08e9692156d21f1f6", size = 660983 },
+ { url = "https://files.pythonhosted.org/packages/f6/e8/1ca6a2d59562e04d326a026c9e3f791a6f1a276ebde29da478843a566fdb/pyzmq-26.2.1-cp313-cp313t-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:90dc731d8e3e91bcd456aa7407d2eba7ac6f7860e89f3766baabb521f2c1de4a", size = 896509 },
+ { url = "https://files.pythonhosted.org/packages/5c/e5/0b4688f7c74bea7e4f1e920da973fcd7d20175f4f1181cb9b692429c6bb9/pyzmq-26.2.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0b6a93d684278ad865fc0b9e89fe33f6ea72d36da0e842143891278ff7fd89c3", size = 853196 },
+ { url = "https://files.pythonhosted.org/packages/8f/35/c17241da01195001828319e98517683dad0ac4df6fcba68763d61b630390/pyzmq-26.2.1-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:c1bb37849e2294d519117dd99b613c5177934e5c04a5bb05dd573fa42026567e", size = 855133 },
+ { url = "https://files.pythonhosted.org/packages/d2/14/268ee49bbecc3f72e225addeac7f0e2bd5808747b78c7bf7f87ed9f9d5a8/pyzmq-26.2.1-cp313-cp313t-musllinux_1_1_aarch64.whl", hash = "sha256:632a09c6d8af17b678d84df442e9c3ad8e4949c109e48a72f805b22506c4afa7", size = 1191612 },
+ { url = "https://files.pythonhosted.org/packages/5e/02/6394498620b1b4349b95c534f3ebc3aef95f39afbdced5ed7ee315c49c14/pyzmq-26.2.1-cp313-cp313t-musllinux_1_1_i686.whl", hash = "sha256:fc409c18884eaf9ddde516d53af4f2db64a8bc7d81b1a0c274b8aa4e929958e8", size = 1500824 },
+ { url = "https://files.pythonhosted.org/packages/17/fc/b79f0b72891cbb9917698add0fede71dfb64e83fa3481a02ed0e78c34be7/pyzmq-26.2.1-cp313-cp313t-musllinux_1_1_x86_64.whl", hash = "sha256:17f88622b848805d3f6427ce1ad5a2aa3cf61f12a97e684dab2979802024d460", size = 1399943 },
+]
+
[[package]]
name = "referencing"
version = "0.36.2"
@@ -1301,6 +2003,27 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6", size = 64928 },
]
+[[package]]
+name = "rfc3339-validator"
+version = "0.1.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "six" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/28/ea/a9387748e2d111c3c2b275ba970b735e04e15cdb1eb30693b6b5708c4dbd/rfc3339_validator-0.1.4.tar.gz", hash = "sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b", size = 5513 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/7b/44/4e421b96b67b2daff264473f7465db72fbdf36a07e05494f50300cc7b0c6/rfc3339_validator-0.1.4-py2.py3-none-any.whl", hash = "sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa", size = 3490 },
+]
+
+[[package]]
+name = "rfc3986-validator"
+version = "0.1.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/da/88/f270de456dd7d11dcc808abfa291ecdd3f45ff44e3b549ffa01b126464d0/rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055", size = 6760 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl", hash = "sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9", size = 4242 },
+]
+
[[package]]
name = "rpds-py"
version = "0.22.3"
@@ -1417,6 +2140,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987", size = 294914 },
]
+[[package]]
+name = "send2trash"
+version = "1.8.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fd/3a/aec9b02217bb79b87bbc1a21bc6abc51e3d5dcf65c30487ac96c0908c722/Send2Trash-1.8.3.tar.gz", hash = "sha256:b18e7a3966d99871aefeb00cfbcfdced55ce4871194810fc71f4aa484b953abf", size = 17394 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/40/b0/4562db6223154aa4e22f939003cb92514c79f3d4dccca3444253fd17f902/Send2Trash-1.8.3-py3-none-any.whl", hash = "sha256:0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9", size = 18072 },
+]
+
[[package]]
name = "setuptools"
version = "75.8.0"
@@ -1444,6 +2176,24 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/04/be/d09147ad1ec7934636ad912901c5fd7667e1c858e19d355237db0d0cd5e4/smmap-5.0.2-py3-none-any.whl", hash = "sha256:b30115f0def7d7531d22a0fb6502488d879e75b260a9db4d0819cfb25403af5e", size = 24303 },
]
+[[package]]
+name = "sniffio"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a2/87/a6771e1546d97e7e041b6ae58d80074f81b7d5121207425c964ddf5cfdbd/sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc", size = 20372 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2", size = 10235 },
+]
+
+[[package]]
+name = "soupsieve"
+version = "2.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d7/ce/fbaeed4f9fb8b2daa961f90591662df6a86c1abf25c548329a86920aedfb/soupsieve-2.6.tar.gz", hash = "sha256:e2e68417777af359ec65daac1057404a3c8a5455bb8abc36f1a9866ab1a51abb", size = 101569 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/d1/c2/fe97d779f3ef3b15f05c94a2f1e3d21732574ed441687474db9d342a7315/soupsieve-2.6-py3-none-any.whl", hash = "sha256:e72c4ff06e4fb6e4b5a9f0f55fe6e81514581fca1515028625d0f299c602ccc9", size = 36186 },
+]
+
[[package]]
name = "stack-data"
version = "0.6.3"
@@ -1479,6 +2229,32 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/b6/cb/b86984bed139586d01532a587464b5805f12e397594f19f931c4c2fbfa61/tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539", size = 28169 },
]
+[[package]]
+name = "terminado"
+version = "0.18.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "ptyprocess", marker = "os_name != 'nt'" },
+ { name = "pywinpty", marker = "os_name == 'nt'" },
+ { name = "tornado" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8a/11/965c6fd8e5cc254f1fe142d547387da17a8ebfd75a3455f637c663fb38a0/terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e", size = 32701 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/6a/9e/2064975477fdc887e47ad42157e214526dcad8f317a948dee17e1659a62f/terminado-0.18.1-py3-none-any.whl", hash = "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0", size = 14154 },
+]
+
+[[package]]
+name = "tinycss2"
+version = "1.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "webencodings" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7a/fd/7a5ee21fd08ff70d3d33a5781c255cbe779659bd03278feb98b19ee550f4/tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7", size = 87085 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/e6/34/ebdc18bae6aa14fbee1a08b63c015c72b64868ff7dae68808ab500c492e2/tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289", size = 26610 },
+]
+
[[package]]
name = "torch"
version = "2.4.1"
@@ -1514,6 +2290,24 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/ac/30/8b6f77ea4ce84f015ee024b8dfef0dac289396254e8bfd493906d4cbb848/torch-2.4.1-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:72b484d5b6cec1a735bf3fa5a1c4883d01748698c5e9cfdbeb4ffab7c7987e0d", size = 62123443 },
]
+[[package]]
+name = "tornado"
+version = "6.4.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/59/45/a0daf161f7d6f36c3ea5fc0c2de619746cc3dd4c76402e9db545bd920f63/tornado-6.4.2.tar.gz", hash = "sha256:92bad5b4746e9879fd7bf1eb21dce4e3fc5128d71601f80005afa39237ad620b", size = 501135 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/26/7e/71f604d8cea1b58f82ba3590290b66da1e72d840aeb37e0d5f7291bd30db/tornado-6.4.2-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:e828cce1123e9e44ae2a50a9de3055497ab1d0aeb440c5ac23064d9e44880da1", size = 436299 },
+ { url = "https://files.pythonhosted.org/packages/96/44/87543a3b99016d0bf54fdaab30d24bf0af2e848f1d13d34a3a5380aabe16/tornado-6.4.2-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:072ce12ada169c5b00b7d92a99ba089447ccc993ea2143c9ede887e0937aa803", size = 434253 },
+ { url = "https://files.pythonhosted.org/packages/cb/fb/fdf679b4ce51bcb7210801ef4f11fdac96e9885daa402861751353beea6e/tornado-6.4.2-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1a017d239bd1bb0919f72af256a970624241f070496635784d9bf0db640d3fec", size = 437602 },
+ { url = "https://files.pythonhosted.org/packages/4f/3b/e31aeffffc22b475a64dbeb273026a21b5b566f74dee48742817626c47dc/tornado-6.4.2-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c36e62ce8f63409301537222faffcef7dfc5284f27eec227389f2ad11b09d946", size = 436972 },
+ { url = "https://files.pythonhosted.org/packages/22/55/b78a464de78051a30599ceb6983b01d8f732e6f69bf37b4ed07f642ac0fc/tornado-6.4.2-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bca9eb02196e789c9cb5c3c7c0f04fb447dc2adffd95265b2c7223a8a615ccbf", size = 437173 },
+ { url = "https://files.pythonhosted.org/packages/79/5e/be4fb0d1684eb822c9a62fb18a3e44a06188f78aa466b2ad991d2ee31104/tornado-6.4.2-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:304463bd0772442ff4d0f5149c6f1c2135a1fae045adf070821c6cdc76980634", size = 437892 },
+ { url = "https://files.pythonhosted.org/packages/f5/33/4f91fdd94ea36e1d796147003b490fe60a0215ac5737b6f9c65e160d4fe0/tornado-6.4.2-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:c82c46813ba483a385ab2a99caeaedf92585a1f90defb5693351fa7e4ea0bf73", size = 437334 },
+ { url = "https://files.pythonhosted.org/packages/2b/ae/c1b22d4524b0e10da2f29a176fb2890386f7bd1f63aacf186444873a88a0/tornado-6.4.2-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:932d195ca9015956fa502c6b56af9eb06106140d844a335590c1ec7f5277d10c", size = 437261 },
+ { url = "https://files.pythonhosted.org/packages/b5/25/36dbd49ab6d179bcfc4c6c093a51795a4f3bed380543a8242ac3517a1751/tornado-6.4.2-cp38-abi3-win32.whl", hash = "sha256:2876cef82e6c5978fde1e0d5b1f919d756968d5b4282418f3146b79b58556482", size = 438463 },
+ { url = "https://files.pythonhosted.org/packages/61/cc/58b1adeb1bb46228442081e746fcdbc4540905c87e8add7c277540934edb/tornado-6.4.2-cp38-abi3-win_amd64.whl", hash = "sha256:908b71bf3ff37d81073356a5fadcc660eb10c1476ee6e2725588626ce7e5ca38", size = 438907 },
+]
+
[[package]]
name = "tqdm"
version = "4.66.6"
@@ -1547,6 +2341,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/fe/7b/7757205dee3628f75e7991021d15cd1bd0c9b044ca9affe99b50879fc0e1/triton-3.0.0-1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:34e509deb77f1c067d8640725ef00c5cbfcb2052a1a3cb6a6d343841f92624eb", size = 209464695 },
]
+[[package]]
+name = "types-python-dateutil"
+version = "2.9.0.20241206"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a9/60/47d92293d9bc521cd2301e423a358abfac0ad409b3a1606d8fbae1321961/types_python_dateutil-2.9.0.20241206.tar.gz", hash = "sha256:18f493414c26ffba692a72369fea7a154c502646301ebfe3d56a04b3767284cb", size = 13802 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/0f/b3/ca41df24db5eb99b00d97f89d7674a90cb6b3134c52fb8121b6d8d30f15c/types_python_dateutil-2.9.0.20241206-py3-none-any.whl", hash = "sha256:e248a4bc70a486d3e3ec84d0dc30eec3a5f979d6e7ee4123ae043eedbb987f53", size = 14384 },
+]
+
[[package]]
name = "typing-extensions"
version = "4.12.2"
@@ -1565,6 +2368,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/0f/dd/84f10e23edd882c6f968c21c2434fe67bd4a528967067515feca9e611e5e/tzdata-2025.1-py2.py3-none-any.whl", hash = "sha256:7e127113816800496f027041c570f50bcd464a020098a3b6b199517772303639", size = 346762 },
]
+[[package]]
+name = "uri-template"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/31/c7/0336f2bd0bcbada6ccef7aaa25e443c118a704f828a0620c6fa0207c1b64/uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7", size = 21678 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363", size = 11140 },
+]
+
[[package]]
name = "urllib3"
version = "2.3.0"
@@ -1582,3 +2394,39 @@ sdist = { url = "https://files.pythonhosted.org/packages/6c/63/53559446a878410fc
wheels = [
{ url = "https://files.pythonhosted.org/packages/fd/84/fd2ba7aafacbad3c4201d395674fc6348826569da3c0937e75505ead3528/wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859", size = 34166 },
]
+
+[[package]]
+name = "webcolors"
+version = "24.11.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7b/29/061ec845fb58521848f3739e466efd8250b4b7b98c1b6c5bf4d40b419b7e/webcolors-24.11.1.tar.gz", hash = "sha256:ecb3d768f32202af770477b8b65f318fa4f566c22948673a977b00d589dd80f6", size = 45064 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/60/e8/c0e05e4684d13459f93d312077a9a2efbe04d59c393bc2b8802248c908d4/webcolors-24.11.1-py3-none-any.whl", hash = "sha256:515291393b4cdf0eb19c155749a096f779f7d909f7cceea072791cb9095b92e9", size = 14934 },
+]
+
+[[package]]
+name = "webencodings"
+version = "0.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/02/ae6ceac1baeda530866a85075641cec12989bd8d31af6d5ab4a3e8c92f47/webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923", size = 9721 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", size = 11774 },
+]
+
+[[package]]
+name = "websocket-client"
+version = "1.8.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e6/30/fba0d96b4b5fbf5948ed3f4681f7da2f9f64512e1d303f94b4cc174c24a5/websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da", size = 54648 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/5a/84/44687a29792a70e111c5c477230a72c4b957d88d16141199bf9acb7537a3/websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526", size = 58826 },
+]
+
+[[package]]
+name = "widgetsnbextension"
+version = "4.0.13"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/56/fc/238c424fd7f4ebb25f8b1da9a934a3ad7c848286732ae04263661eb0fc03/widgetsnbextension-4.0.13.tar.gz", hash = "sha256:ffcb67bc9febd10234a362795f643927f4e0c05d9342c727b65d2384f8feacb6", size = 1164730 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/21/02/88b65cc394961a60c43c70517066b6b679738caf78506a5da7b88ffcb643/widgetsnbextension-4.0.13-py3-none-any.whl", hash = "sha256:74b2692e8500525cc38c2b877236ba51d34541e6385eeed5aec15a70f88a6c71", size = 2335872 },
+]