diff --git a/.idea/Project_AI.iml b/.idea/Project_AI.iml
index 94259c3..dd7b1a8 100644
--- a/.idea/Project_AI.iml
+++ b/.idea/Project_AI.iml
@@ -4,13 +4,20 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.8 (base)" jdkType="Python SDK" />
+    <orderEntry type="inheritedJdk" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyDocumentationSettings">
     <option name="format" value="PLAIN" />
     <option name="myDocStringFormat" value="Plain" />
   </component>
+  <component name="PyNamespacePackagesService">
+    <option name="namespacePackageFolders">
+      <list>
+        <option value="$MODULE_DIR$/DQN_mulit_tensorflow_2" />
+      </list>
+    </option>
+  </component>
   <component name="TemplatesService">
     <option name="TEMPLATE_CONFIGURATION" value="Jinja2" />
   </component>
diff --git a/.idea/misc.xml b/.idea/misc.xml
index c3334de..ee9b51e 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (base)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (Project_AI) (2)" project-jdk-type="Python SDK" />
 </project>
\ No newline at end of file
diff --git a/DQN_mulit_tensorflow_2/DQNAgent_ddqn.py b/DQN_mulit_tensorflow_2/DQNAgent_ddqn.py
new file mode 100644
index 0000000..fa87d8a
--- /dev/null
+++ b/DQN_mulit_tensorflow_2/DQNAgent_ddqn.py
@@ -0,0 +1,150 @@
+import tensorflow.keras as keras
+from keras.layers import Dense, Flatten, Conv2D
+from keras import Sequential
+from tensorflow.keras.optimizers import Adam
+from pommerman.agents import BaseAgent
+from pommerman.agents.simple_agent import SimpleAgent
+from pommerman import characters
+
+from gym.spaces import Discrete
+
+import constants
+from replay_memory import replay_Memory
+import numpy as np
+import tensorflow as tf
+
+
+class DQNAgent(BaseAgent):
+    """DQN second try with keras"""
+
+    def __init__(self, character=characters.Bomber):
+        super(DQNAgent, self).__init__(character)
+        self.baseAgent = SimpleAgent()
+
+        self.training_model = self.new_model()
+        self.trained_model = self.new_model()
+
+        self.trained_model.set_weights(self.training_model.get_weights())
+        #self.load_weights()
+
+        self.epsilon = constants.epsilon
+        self.min_epsilon = constants.MIN_EPSILON
+        self.eps_decay = constants.EPSILON_DECAY
+        self.buffer = replay_Memory(constants.MAX_BUFFER_SIZE)
+        self.update_counter = 0
+        self.V = keras.layers.Dense(1,activation=None)
+        self.A = keras.layers.Dense(6,activation=None)
+
+    def new_model(self):
+
+        model = Sequential()
+        input_shape = (constants.MINIBATCH_SIZE, 18, 11, 11,)
+        model.add(Conv2D(256, 3, (1, 1), input_shape=input_shape[1:], activation="relu", data_format="channels_first",
+                         padding="same"))
+        # print(model.output_shape)
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+        # print(model.output_shape)
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+        # print(model.output_shape)
+
+        model.add(Flatten())
+        model.add(Dense(128, activation="relu"))
+        model.add(Dense(64, activation='linear'))
+        model.compile(loss="mse", optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])
+        model.summary()
+        return model
+
+    def dueling(self, state):
+        V = self.V(state)
+        # advantage value
+        A = self.A(state)
+        mean = tf.math.reduce_mean(A, axis=1, keepdims=True)
+        # output
+        output = V + (A - mean)
+        return output
+    
+    def advantage(self, state):
+        A = self.A(state)
+        return A
+
+    def act(self, obs, action_space):
+        return self.baseAgent.act(obs, Discrete(6))
+
+    def train(self):
+
+        if self.buffer.size() < constants.MIN_REPLAY_MEMORY_SIZE:
+            return
+        
+        #dueling
+        
+        current_states, action, reward, new_states, done = self.buffer.sample_element(constants.MINIBATCH_SIZE)
+
+        # 在样品中取 current_states, 从模型中获取Q值
+        current_states_q = self.dueling(self.training_model.predict(current_states))
+        double_new_q = self.dueling(self.training_model.predict(new_states))
+        # 在样品中取 next_state, 从旧网络中获取Q值
+        new_states_q = self.dueling(self.trained_model.predict(new_states))
+        
+        # X为state，Y为所预测的action
+        states = []
+        actions = []
+
+        for index in range(constants.MINIBATCH_SIZE):
+
+            if done[index] != True:
+                # 更新Q值
+                #new_state_q = reward[index] + constants.DISCOUNT * np.max(new_states_q[index])
+                double_new_q = reward[index] + constants.DISCOUNT * new_states_q[index][np.argmax(double_new_q[index])]
+            else:
+                #new_state_q = reward[index]
+                double_new_q = reward[index]
+            # 在给定的states下更新Q值
+            current_better_q = current_states_q[index]
+            current_better_q[action[index]] = double_new_q
+
+            # 添加训练数据
+            states.append(current_states[index])
+            actions.append(current_better_q)
+
+            # 开始训练
+        # 使用专用的数据api，但更慢.
+        # states = tf.reshape(states, (-1, 12, 8, 8))
+        # train_dataset = tf.data.Dataset.from_tensor_slices((states, actions))
+        # self.training_model.fit(train_dataset, verbose=0, shuffle=False)
+
+        self.training_model.fit(np.array(states), np.array(actions), batch_size=constants.MINIBATCH_SIZE, verbose=0,
+                                shuffle=False)
+
+        # 更新网络更新计数器
+        if done:
+            self.update_counter += 1
+
+        # 网络更新计数器达到上限，更新网络
+        if self.update_counter > constants.UPDATE_EVERY:
+            self.trained_model.set_weights(self.training_model.get_weights())
+            self.update_counter = 0
+
+    def action_choose(self, state):
+        state_reshape = tf.reshape(state, (-1, 18, 11, 11))
+        q_table = self.advantage(self.training_model.predict(state_reshape))
+        return q_table
+        # epsilon衰减
+
+    def epsilon_decay(self):
+        self.epsilon = self.epsilon * self.eps_decay if self.epsilon > self.min_epsilon else self.epsilon
+
+    def save_weights(self, numOfEpisode):
+
+        # 完成训练后存档参数
+        if numOfEpisode % 200 == 0:
+            self.training_model.save_weights(('./checkpoints/FFA{:}/FFA{:}'.format(numOfEpisode, numOfEpisode)))
+            # self.training_model.save_weights(('./checkpoints/FFA-test-1/FFA-test-1'.format(numOfEpisode, numOfEpisode)))
+            print("weights saved!")
+
+    def load_weights(self):
+        self.training_model.load_weights('./checkpoints/FFA2200/FFA2200')
+        self.trained_model.load_weights('./checkpoints/FFA2200/FFA2200')
+        print("weights loaded!")
+
+    def save_model(self):
+        self.training_model.save("./second_model")
diff --git a/DQN_mulit_tensorflow_2/DQNAgent_modified.py b/DQN_mulit_tensorflow_2/DQNAgent_modified.py
new file mode 100644
index 0000000..116c7b9
--- /dev/null
+++ b/DQN_mulit_tensorflow_2/DQNAgent_modified.py
@@ -0,0 +1,133 @@
+from keras.layers import Dense, Flatten, Conv2D
+from keras import Sequential
+from tensorflow.keras.optimizers import Adam
+from pommerman.agents import BaseAgent
+from pommerman.agents.simple_agent import SimpleAgent
+from pommerman import characters
+
+from gym.spaces import Discrete
+
+from DQN_mulit_tensorflow_2 import constants
+from replay_memory import replay_Memory
+import numpy as np
+import tensorflow as tf
+
+
+class DQNAgent(BaseAgent):
+    """DQN second try with keras"""
+
+    def __init__(self, character=characters.Bomber):
+        super(DQNAgent, self).__init__(character)
+        self.baseAgent = SimpleAgent()
+
+        self.training_model = self.new_model()
+        self.trained_model = self.new_model()
+
+        self.trained_model.set_weights(self.training_model.get_weights())
+        self.load_weights()
+
+        self.epsilon = constants.epsilon
+        self.min_epsilon = constants.MIN_EPSILON
+        self.eps_decay = constants.EPSILON_DECAY
+        self.buffer = replay_Memory(constants.MAX_BUFFER_SIZE)
+        self.update_counter = 0
+
+    def new_model(self):
+
+        model = Sequential()
+        input_shape = (constants.MINIBATCH_SIZE, 18, 11, 11,)
+        model.add(Conv2D(256, 3, (1, 1), input_shape=input_shape[1:], activation="relu", data_format="channels_first",
+                         padding="same"))
+        # print(model.output_shape)
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+        # print(model.output_shape)
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+        # print(model.output_shape)
+
+        model.add(Flatten())
+        model.add(Dense(128, activation="relu"))
+        model.add(Dense(6, activation='linear'))
+        model.compile(loss="mse", optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])
+        model.summary()
+        return model
+
+
+
+    def act(self, obs, action_space):
+        return self.baseAgent.act(obs, Discrete(6))
+
+    def train(self):
+
+        if self.buffer.size() < constants.MIN_REPLAY_MEMORY_SIZE:
+            return
+        
+        current_states, action, reward, new_states, done = self.buffer.sample_element(constants.MINIBATCH_SIZE)
+
+        # 在样品中取 current_states, 从模型中获取Q值
+        current_states_q = self.training_model.predict(current_states)
+        double_new_q = self.training_model.predict(new_states)
+        # 在样品中取 next_state, 从旧网络中获取Q值
+        new_states_q = self.trained_model.predict(new_states)
+        
+        # X为state，Y为所预测的action
+        states = []
+        actions = []
+
+        for index in range(constants.MINIBATCH_SIZE):
+
+            if done[index] != True:
+                # 更新Q值
+                new_state_q = reward[index] + constants.DISCOUNT * np.max(new_states_q[index])
+                double_new_q = reward[index] + constants.DISCOUNT * new_states_q[index][np.argmax(double_new_q[index])]
+            else:
+                new_state_q = reward[index]
+            # 在给定的states下更新Q值
+            current_better_q = current_states_q[index]
+            current_better_q[action[index]] = new_state_q
+
+            # 添加训练数据
+            states.append(current_states[index])
+            actions.append(current_better_q)
+
+            # 开始训练
+        # 使用专用的数据api，但更慢.
+        # states = tf.reshape(states, (-1, 12, 8, 8))
+        # train_dataset = tf.data.Dataset.from_tensor_slices((states, actions))
+        # self.training_model.fit(train_dataset, verbose=0, shuffle=False)
+
+        self.training_model.fit(np.array(states), np.array(actions), batch_size=constants.MINIBATCH_SIZE, verbose=0,
+                                shuffle=False)
+
+        # 更新网络更新计数器
+        if done:
+            self.update_counter += 1
+
+        # 网络更新计数器达到上限，更新网络
+        if self.update_counter > constants.UPDATE_EVERY:
+            self.trained_model.set_weights(self.training_model.get_weights())
+            self.update_counter = 0
+
+    def action_choose(self, state):
+        state_reshape = tf.reshape(state, (-1, 18, 11, 11))
+        q_table = self.training_model.predict(state_reshape)
+        return q_table
+        # epsilon衰减
+
+    def epsilon_decay(self):
+        self.epsilon = self.epsilon * self.eps_decay if self.epsilon > self.min_epsilon else self.epsilon
+
+    def save_weights(self, numOfEpisode):
+
+        # 完成训练后存档参数
+        if numOfEpisode % 200 == 0:
+            self.training_model.save_weights(('./checkpoints/FFA{:}/FFA{:}'.format(numOfEpisode, numOfEpisode)))
+            # self.training_model.save_weights(('./checkpoints/FFA-test-1/FFA-test-1'.format(numOfEpisode, numOfEpisode)))
+            print("weights saved!")
+
+    def load_weights(self):
+        self.training_model.load_weights('./checkpoints/FFA2200/FFA2200')
+        self.trained_model.load_weights('./checkpoints/FFA2200/FFA2200')
+        print("weights loaded!")
+
+    def save_model(self):
+        self.training_model.save("./second_model")
diff --git a/DQN_mulit_tensorflow_2/DQNAgent_modified_nhwc.py b/DQN_mulit_tensorflow_2/DQNAgent_modified_nhwc.py
new file mode 100644
index 0000000..808c6e2
--- /dev/null
+++ b/DQN_mulit_tensorflow_2/DQNAgent_modified_nhwc.py
@@ -0,0 +1,137 @@
+from keras.layers import Dense, Flatten, Conv2D
+from keras import Sequential
+from tensorflow.keras.optimizers import Adam
+from pommerman.agents import BaseAgent
+from pommerman.agents.simple_agent import SimpleAgent
+from pommerman import characters
+
+from gym.spaces import Discrete
+
+from DQN_mulit_tensorflow_2 import constants
+from replay_memory import replay_Memory
+import numpy as np
+import tensorflow as tf
+import time
+import os
+
+
+class DQNAgent(BaseAgent):
+    """DQN second try with keras"""
+
+    def __init__(self, character=characters.Bomber):
+        super(DQNAgent, self).__init__(character)
+        self.baseAgent = SimpleAgent()
+        #self.cuda = os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+        self.training_model = self.new_model()
+        self.trained_model = self.new_model()
+
+        self.trained_model.set_weights(self.training_model.get_weights())
+        # self.load_weights()
+
+        self.epsilon = constants.epsilon
+        self.min_epsilon = constants.MIN_EPSILON
+        self.eps_decay = constants.EPSILON_DECAY
+        self.buffer = replay_Memory(constants.MAX_BUFFER_SIZE)
+        self.update_counter = 0
+
+    def new_model(self):
+
+        model = Sequential()
+        input_shape = (constants.MINIBATCH_SIZE, 11, 11, 18)
+        model.add(Conv2D(256, 3, (1, 1), input_shape=input_shape[1:], activation="relu", padding="same"))
+        # print(model.output_shape)
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", padding="same"))
+        # print(model.output_shape)
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", padding="same"))
+        # print(model.output_shape)
+
+        model.add(Flatten())
+        model.add(Dense(128, activation="relu"))
+        model.add(Dense(6, activation='linear'))
+        model.compile(loss="mse", optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])
+        model.summary()
+        return model
+
+    def act(self, obs, action_space):
+        return self.baseAgent.act(obs, Discrete(6))
+
+    def train(self):
+
+        if self.buffer.size() < constants.MIN_REPLAY_MEMORY_SIZE:
+            return
+
+        current_states, action, reward, new_states, done = self.buffer.sample_element(constants.MINIBATCH_SIZE)
+        # start_time = time.time()
+        current_states_shaped = tf.transpose(current_states, [0, 2, 3, 1])
+        new_states_shaped = tf.transpose(new_states, [0, 2, 3, 1])
+        # end_time = time.time()
+        # print("transform time: ",start_time-end_time)
+        # 在样品中取 current_states, 从模型中获取Q值
+        current_states_q = self.training_model.predict(current_states_shaped)
+
+        # 在样品中取 next_state, 从旧网络中获取Q值
+        new_states_q = self.trained_model.predict(new_states_shaped)
+
+        # X为state，Y为所预测的action
+        states = []
+        actions = []
+
+        for index in range(constants.MINIBATCH_SIZE):
+
+            if done[index] != True:
+                # 更新Q值
+                new_state_q = reward[index] + constants.DISCOUNT * np.max(new_states_q[index])
+            else:
+                new_state_q = reward[index]
+            # 在给定的states下更新Q值
+            current_better_q = current_states_q[index]
+            current_better_q[action[index]] = new_state_q
+
+            # 添加训练数据
+            states.append(current_states[index])
+            # states.append(tf.reshape(current_state,(-1,14,11,11)))
+            actions.append(current_better_q)
+
+            # 开始训练
+        # 使用专用的数据api，但更慢.
+        # states = tf.reshape(states, (-1, 12, 8, 8))
+        # train_dataset = tf.data.Dataset.from_tensor_slices((states, actions))
+        # self.training_model.fit(train_dataset, verbose=0, shuffle=False)
+        states = tf.transpose(states, [0, 2, 3, 1])
+        self.training_model.fit(np.array(states), np.array(actions), batch_size=constants.MINIBATCH_SIZE, verbose=0,
+                                shuffle=False)
+
+        # 更新网络更新计数器
+        if done:
+            self.update_counter += 1
+
+        # 网络更新计数器达到上限，更新网络
+        if self.update_counter > constants.UPDATE_EVERY:
+            self.trained_model.set_weights(self.training_model.get_weights())
+            self.update_counter = 0
+
+    def action_choose(self, state):
+        # start_time = time.time()
+        state_reshape = tf.reshape(state, (-1, 11, 11, 18))
+        q_table = self.training_model.predict(state_reshape)
+        # end_time = time.time()
+        # print("action time: ", start_time - end_time)
+        return q_table
+        # epsilon衰减
+
+    def epsilon_decay(self):
+        self.epsilon = self.epsilon * self.eps_decay if self.epsilon > self.min_epsilon else self.epsilon
+
+    def save_weights(self, numOfEpisode):
+
+        # 完成训练后存档参数
+        if numOfEpisode % 200 == 0:
+            self.training_model.save_weights(('./checkpoints/FFA{:}/FFA{:}'.format(numOfEpisode, numOfEpisode)))
+            # self.training_model.save_weights(('./checkpoints/FFA-test-1/FFA-test-1'.format(numOfEpisode, numOfEpisode)))
+            print("weights saved!")
+
+    def load_weights(self):
+        self.training_model.load_weights('./checkpoints/FFA2200/FFA2200')
+        self.trained_model.load_weights('./checkpoints/FFA2200/FFA2200')
+        print("weights loaded!")
+
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new file mode 100644
index 0000000..29be90e
--- /dev/null
+++ b/DQN_mulit_tensorflow_2/backup/1/FFA1000/checkpoint
@@ -0,0 +1,2 @@
+model_checkpoint_path: "FFA1000"
+all_model_checkpoint_paths: "FFA1000"
diff --git a/DQN_mulit_tensorflow_2/backup/1/FFA1500/FFA1500.data-00000-of-00001 b/DQN_mulit_tensorflow_2/backup/1/FFA1500/FFA1500.data-00000-of-00001
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index 0000000..0b412a9
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diff --git "a/DQN_mulit_tensorflow_2/backup/1/\346\226\260\345\273\272\346\226\207\346\234\254\346\226\207\346\241\243.txt" "b/DQN_mulit_tensorflow_2/backup/1/\346\226\260\345\273\272\346\226\207\346\234\254\346\226\207\346\241\243.txt"
new file mode 100644
index 0000000..54ce7a3
--- /dev/null
+++ "b/DQN_mulit_tensorflow_2/backup/1/\346\226\260\345\273\272\346\226\207\346\234\254\346\226\207\346\241\243.txt"
@@ -0,0 +1,3 @@
+前4000ep 的reward* 1.5
+4000ep 开始 平局不作数
+5000ep 开始 修复reward fail计算两次的问题
\ No newline at end of file
diff --git a/DQN_mulit_tensorflow_2/backup/2/FFA1000/FFA1000.data-00000-of-00001 b/DQN_mulit_tensorflow_2/backup/2/FFA1000/FFA1000.data-00000-of-00001
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new file mode 100644
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--- /dev/null
+++ b/DQN_mulit_tensorflow_2/backup/2/FFA1000/checkpoint
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diff --git "a/DQN_mulit_tensorflow_2/backup/2/\346\226\260\345\273\272\346\226\207\346\234\254\346\226\207\346\241\243.txt" "b/DQN_mulit_tensorflow_2/backup/2/\346\226\260\345\273\272\346\226\207\346\234\254\346\226\207\346\241\243.txt"
new file mode 100644
index 0000000..590018e
--- /dev/null
+++ "b/DQN_mulit_tensorflow_2/backup/2/\346\226\260\345\273\272\346\226\207\346\234\254\346\226\207\346\241\243.txt"
@@ -0,0 +1,8 @@
+r_wood = 0.3
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+    r_put_bomb_near_enemy = 1
+    r_kick = 0.5
+    r_in_flame = -10
+    r_move = -0.02
+    r_stay = -0.04
\ No newline at end of file
diff --git a/DQN_mulit_tensorflow_2/backup/3/FFA100/FFA100.data-00000-of-00001 b/DQN_mulit_tensorflow_2/backup/3/FFA100/FFA100.data-00000-of-00001
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new file mode 100644
index 0000000..30c10a2
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diff --git "a/DQN_mulit_tensorflow_2/backup/4/\346\226\260\345\273\272\346\226\207\346\234\254\346\226\207\346\241\243.txt" "b/DQN_mulit_tensorflow_2/backup/4/\346\226\260\345\273\272\346\226\207\346\234\254\346\226\207\346\241\243.txt"
new file mode 100644
index 0000000..31db43f
--- /dev/null
+++ "b/DQN_mulit_tensorflow_2/backup/4/\346\226\260\345\273\272\346\226\207\346\234\254\346\226\207\346\241\243.txt"
@@ -0,0 +1,11 @@
+ r_wood = 0.3
+    r_powerup = 0.5
+    r_put_bomb = 0.3
+    r_put_bomb_near_enemy = 1
+    r_kick = 0.5
+    r_in_flame = -10
+    r_move = 0.02
+    r_stay = -0.04
+
+    reward = 0
+ 调整网络前
\ No newline at end of file
diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA1000/FFA1000.data-00000-of-00001 b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA1000/FFA1000.data-00000-of-00001
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diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA1000/FFA1000.index b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA1000/FFA1000.index
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diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA1000/checkpoint b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA1000/checkpoint
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index 0000000..29be90e
--- /dev/null
+++ b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA1000/checkpoint	
@@ -0,0 +1,2 @@
+model_checkpoint_path: "FFA1000"
+all_model_checkpoint_paths: "FFA1000"
diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA1500/FFA1500.data-00000-of-00001 b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA1500/FFA1500.data-00000-of-00001
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diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA1500/FFA1500.index b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA1500/FFA1500.index
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diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA1500/checkpoint b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA1500/checkpoint
new file mode 100644
index 0000000..b7b6bee
--- /dev/null
+++ b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA1500/checkpoint	
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+model_checkpoint_path: "FFA1500"
+all_model_checkpoint_paths: "FFA1500"
diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA2000/FFA2000.data-00000-of-00001 b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA2000/FFA2000.data-00000-of-00001
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diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA2000/FFA2000.index b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA2000/FFA2000.index
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diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA2000/checkpoint b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA2000/checkpoint
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index 0000000..1dcab0c
--- /dev/null
+++ b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA2000/checkpoint	
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+model_checkpoint_path: "FFA2000"
+all_model_checkpoint_paths: "FFA2000"
diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA2500/FFA2500.data-00000-of-00001 b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA2500/FFA2500.data-00000-of-00001
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diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA2500/FFA2500.index b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA2500/FFA2500.index
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diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA2500/checkpoint b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA2500/checkpoint
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--- /dev/null
+++ b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA2500/checkpoint	
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+model_checkpoint_path: "FFA2500"
+all_model_checkpoint_paths: "FFA2500"
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--- /dev/null
+++ b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA3000/checkpoint	
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+model_checkpoint_path: "FFA3000"
+all_model_checkpoint_paths: "FFA3000"
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diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA500/FFA500.index b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA500/FFA500.index
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diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA500/checkpoint b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA500/checkpoint
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--- /dev/null
+++ b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/FFA500/checkpoint	
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+model_checkpoint_path: "FFA500"
+all_model_checkpoint_paths: "FFA500"
diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/result.csv b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/result.csv
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diff --git a/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/reward.csv b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/reward.csv
new file mode 100644
index 0000000..2478e8e
--- /dev/null
+++ b/DQN_mulit_tensorflow_2/backup/FFA1 32(3) 64(3) 64(3) 512 6/reward.csv	
@@ -0,0 +1,3001 @@
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new file mode 100644
index 0000000..3e3ad69
Binary files /dev/null and b/DQN_mulit_tensorflow_2/checkpoints/pre-train/pre-train.index differ
diff --git a/DQN_mulit_tensorflow_2/constants.py b/DQN_mulit_tensorflow_2/constants.py
new file mode 100644
index 0000000..75a2db1
--- /dev/null
+++ b/DQN_mulit_tensorflow_2/constants.py
@@ -0,0 +1,21 @@
+from pommerman import constants
+
+MIN_REPLAY_MEMORY_SIZE = 1000  # Minimum number of steps in a memory to start training
+MINIBATCH_SIZE = 256  # How many steps (samples) to use for training
+UPDATE_EVERY = 20  # Terminal states (end of episodes)
+MAX_BUFFER_SIZE = 100_000
+MAX_BUFFER_SIZE_PRE = 1_000_000
+DISCOUNT = 0.95
+MAX_STEPS = constants.MAX_STEPS
+
+# Environment settings
+EPISODES = 100000
+SHOW_EVERY = 1
+
+# Exploration settings
+epsilon = 0.95  # not a constant, going to be decayed
+EPSILON_DECAY = 0.99975
+MIN_EPSILON = 0.95
+
+SHOW_PREVIEW = True
+SHOW_GAME = 100
diff --git a/DQN_mulit_tensorflow_2/main_FFA.py b/DQN_mulit_tensorflow_2/main_FFA.py
new file mode 100644
index 0000000..885d790
--- /dev/null
+++ b/DQN_mulit_tensorflow_2/main_FFA.py
@@ -0,0 +1,149 @@
+
+import constants
+import pommerman
+import numpy as np
+import pandas as pd
+
+#from DQNAgent_modified import DQNAgent
+from DQNAgent_modified_nhwc import DQNAgent
+from pommerman.agents import SimpleAgent
+from utility import featurize2D, reward_shaping
+
+
+def main():
+    agent1 = DQNAgent()
+    agent2 = SimpleAgent()
+    agent3 = SimpleAgent()
+    agent4 = SimpleAgent()
+
+    agent_list = [agent1, agent2, agent3, agent4]
+    env = pommerman.make('PommeFFACompetitionFast-v0', agent_list)
+
+    episode_rewards = []  # 记录平均reward
+
+    win = 0
+    draw = 0
+    total_game = 0
+    reward_to_csv = []
+    result_to_csv = []
+
+    total_numOfSteps = 0
+    episode = 0
+
+
+    while True:
+
+        current_state = env.reset()
+        # 将state 转化 1D array
+
+        episode_reward = 0
+        numOfSteps = 0
+        episode += 1
+        done = False
+
+        while not done:
+
+            state_feature = featurize2D(current_state[0])
+            numOfSteps += 1
+            total_numOfSteps += 1
+
+            if constants.epsilon > np.random.random() and total_numOfSteps >= constants.MIN_REPLAY_MEMORY_SIZE:
+            #if constants.epsilon > np.random.random():
+                # 获取动作
+                actions = env.act(current_state)
+                actions[0] = np.argmax(agent1.action_choose(state_feature)).tolist()
+            else:
+                # 随机动作
+                actions = env.act(current_state)
+                # actions[0] = random.randint(0, 5)
+
+            new_state, result, done, info = env.step(actions)
+
+            if 10 not in new_state[0]["alive"]:
+                done = True
+
+            # reward_shaping
+            agent1.buffer.append_action(actions[0])
+            reward = reward_shaping(current_state[0], new_state[0], actions[0], result[0], agent1.buffer.buffer_action)
+            # print("reward: ",reward)
+            next_state_feature = featurize2D(new_state[0])
+            episode_reward += reward
+
+            # 每一定局数显示游戏画面
+            # if constants.SHOW_PREVIEW and not episode % constants.SHOW_GAME:
+            # env.render()
+
+            # 储存记忆
+            agent1.buffer.append([state_feature, actions[0], reward, next_state_feature, done])
+
+            # 学习!
+            agent1.train()
+
+            # 更新state
+            current_state = new_state
+
+            if done:
+                break
+
+        result = 0
+
+        if done:
+            episode_rewards.append(episode_reward)
+            total_game += 1
+            if 0 in info.get('winners', []):
+                win += 1
+                result = 2
+
+        # 记录胜负情况
+        if numOfSteps == constants.MAX_STEPS + 1:
+            draw += 1
+            result = 1
+        win_rate = win / total_game
+        draw_rate = draw / total_game
+        # 存reward
+        reward_to_csv.append(episode_reward)
+        # 存result
+        result_to_csv.append(result)
+
+        if episode % constants.SHOW_EVERY == 0:
+            if result == 1:
+                print("{} episodes done, result: {} , steps: {}".format(episode,
+                                                                        'draw',
+                                                                        numOfSteps))
+
+                print("Reward {:.2f}, Average Episode Reward: {:.3f}, win_rate:{:.2f}, draw_rate:{:.2f}".format(
+                    episode_reward,
+                    np.mean(episode_rewards),
+                    win_rate,
+                    draw_rate))
+            else:
+                print("{} episodes done, result: {} , steps: {}".format(episode,
+                                                                        'win' if result == 2 else "lose",
+                                                                        numOfSteps))
+
+                print("Reward {:.3f}, Average Episode Reward: {:.3f}, win_rate:{:.2f}, draw_rate:{:.2f}".format(
+                    episode_reward,
+                    np.mean(episode_rewards),
+                    win_rate,
+                    draw_rate))
+
+        # agent1.epsilon_decay()
+
+        agent1.save_weights(episode)
+
+        # 记录结果，留作图表
+        if episode % 100 == 0:
+            df_reward = pd.DataFrame({"reward": reward_to_csv})
+            df_reward.to_csv("reward.csv", index=False, mode="a", header=False)
+            print("successfully saved reward")
+            reward_to_csv = []
+            df_result = pd.DataFrame({"result": result_to_csv})
+            df_result.to_csv("result.csv", index=False, mode="a", header=False)
+            print("successfully saved result")
+            result_to_csv = []
+
+    env.close()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/DQN_mulit_tensorflow_2/main_test.py b/DQN_mulit_tensorflow_2/main_test.py
new file mode 100644
index 0000000..621d6da
--- /dev/null
+++ b/DQN_mulit_tensorflow_2/main_test.py
@@ -0,0 +1,154 @@
+import time
+
+import constants
+import pommerman
+import numpy as np
+
+# from DQNAgent_radio import DQNAgent
+from DQNAgent_modified import DQNAgent
+# from DQNAgent_one_vs_one import DQNAgent
+# from DQNAgent_dueling_dqn import DQNAgent
+#from DQNAgent_double_dqn import DQNAgent
+# from DQNAgent_dueling_dqn import DQNAgent
+# from DQNAgent_modified_filter_tong import DQNAgent
+# from DQNAgent_radio_filter2 import DQNAgent
+# from DQNAgent_noisy import DQNAgent
+from pommerman.agents import SimpleAgent, RandomAgent
+from utility import featurize2D, reward_shaping
+
+
+# from DQNAgent_radio import DQNAgent
+
+def main():
+    agent1 = DQNAgent()
+    agent2 = SimpleAgent()
+    agent3 = DQNAgent()
+    agent4 = SimpleAgent()
+
+    # agent1 = DQNAgent()
+    # agent2 = DQNAgent()
+    # agent3 = DQNAgent()
+    # agent4 = DQNAgent()
+
+    agent_list = [agent1, agent2, agent3, agent4]
+
+    #env = pommerman.make("PommeFFACompetitionFast-v0", agent_list)
+    env = pommerman.make("PommeRadioCompetition-v2", agent_list)
+
+    episode_rewards = []  # 记录平均reward
+
+    win = 0
+    draw = 0
+    total_game = 0
+
+    total_numOfSteps = 0
+    episode = 0
+    # while True:
+    for i in range(100):
+        # agent1.save_model()
+        current_state = env.reset()
+        # 将state 转化 1D array
+
+        episode_reward = 0
+        numOfSteps = 0
+        episode += 1
+        done = False
+
+        while not done:
+
+            state_feature = featurize2D(current_state[0])
+
+            numOfSteps += 1
+            total_numOfSteps += 1
+            # if numOfSteps % 10 == 0:
+            #     actions = env.act(current_state)
+            #     actions[0] = 5
+            #     print("BOMB!")
+            # if constants.epsilon > np.random.random() and total_numOfSteps >= constants.MIN_REPLAY_MEMORY_SIZE:
+            # if constants.epsilon > np.random.random():
+            #     # 获取动作
+            actions = env.act(current_state)
+            actions[0] = np.argmax(agent1.action_choose(state_feature)).tolist()
+            #actions[0] = agent1.act_filter(current_state[0], actions[0])
+
+            # else:
+            #     # 随机动作
+            #     actions = env.act(current_state)
+            #     print("simple: ", actions[0])
+            #     # actions[0] = random.randint(0, 5)
+
+            new_state, result, done, info = env.step(actions)
+
+            if 10 not in new_state[0]["alive"]:
+                done = True
+
+            # reward_shaping
+            # agent1.buffer.append_action(actions[0])
+            # reward = reward_shaping(current_state[0], new_state[0], actions[0], result[0], agent1.buffer.buffer_action)
+
+            # print("action: ", actions[0], "step_reward: ", reward)
+            # print("step reward: ",reward)
+            # next_state_feature = featurize2D(new_state[0])
+            # episode_reward += reward
+
+            # 每一定局数显示游戏画面
+            # if constants.SHOW_PREVIEW and not episode % constants.SHOW_GAME:
+            env.render()
+            time.sleep(10000)
+
+            # 储存记忆
+            # agent1.buffer.append([state_feature, actions[0], reward, next_state_feature, done])
+
+            # 学习!
+            # agent1.train()
+
+            # 更新state
+            current_state = new_state
+
+            if done:
+                break
+
+        result = 0
+
+        if done:
+            episode_rewards.append(episode_reward)
+            total_game += 1
+            if 0 in info.get('winners', []):
+                win += 1
+                result = 2
+
+        # 记录胜负情况
+        if numOfSteps == constants.MAX_STEPS + 1:
+            draw += 1
+            result = 1
+
+        win_rate = win / total_game
+        draw_rate = draw / total_game
+
+        if episode % constants.SHOW_EVERY == 0:
+            if result == 1:
+                print("{} episodes done, result: {} , steps: {}".format(episode,
+                                                                        'draw',
+                                                                        numOfSteps))
+
+                print("Reward {:.2f}, Average Episode Reward: {:.3f}, win_rate:{:.2f}, draw_rate:{:.2f}".format(
+                    episode_reward,
+                    np.mean(episode_rewards),
+                    win_rate,
+                    draw_rate))
+            else:
+                print("{} episodes done, result: {} , steps: {}".format(episode,
+                                                                        'win' if result == 2 else "lose",
+                                                                        numOfSteps))
+
+                print("Reward {:.3f}, Average Episode Reward: {:.3f}, win_rate:{:.2f}, draw_rate:{:.2f}".format(
+                    episode_reward,
+                    np.mean(episode_rewards),
+                    win_rate,
+                    draw_rate))
+    print("win: ", win, " draw: ", draw)
+    env.close()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/DQN_mulit_tensorflow_2/replay_memory.py b/DQN_mulit_tensorflow_2/replay_memory.py
new file mode 100644
index 0000000..d352fa7
--- /dev/null
+++ b/DQN_mulit_tensorflow_2/replay_memory.py
@@ -0,0 +1,109 @@
+import random
+import collections
+import numpy as np
+import constants
+
+
+class replay_Memory():
+    def __init__(self, MAX_BUFFER_SIZE):
+        self.buffer = collections.deque(maxlen=MAX_BUFFER_SIZE)
+        self.buffer_episode = collections.deque()
+        self.buffer_processing = collections.deque()
+        self.buffer_action = collections.deque([0, 0, 0, 0], maxlen=4)
+        self.buffer_td = collections.deque(maxlen=MAX_BUFFER_SIZE)
+        self.alpha = 0.6
+        self.n_step = 4
+        self.gamma = 0.9
+
+    def append(self, transition):
+        self.buffer.append(transition)
+
+    def append_action(self, action):
+        self.buffer_action.append(action)
+
+    def append_augmentation(self, transition):
+        self.buffer_episode.append(transition)
+
+    def append_processing(self, transition):
+        self.buffer_processing.append(transition)
+
+    def append_pri(self, state, action, reward, next_state, done, td_error):
+        # pri DQN
+        transition = [state, action, reward, next_state, done]
+        self.append_td(td_error)
+        self.buffer.append(transition)
+        return True
+
+    def append_td(self, td_error):
+        self.buffer_td.append(td_error)
+
+    def clear(self):
+        self.buffer_episode.clear()
+        self.buffer_processing.clear()
+
+    def merge(self):
+        for element in self.buffer_processing:
+            self.buffer.append(element)
+
+    def merge_negative(self):
+        for element in self.buffer_processing:
+            if element[2] < 0:
+                self.buffer.append(element)
+
+    def sample(self, batch):
+        mini_batch = random.sample(self.buffer, batch)
+        return mini_batch
+
+    def sample_element(self, batch):
+        mini_batch = random.sample(self.buffer, batch)
+        current_state, action, reward, new_states, done = [], [], [], [], []
+
+        for transition in mini_batch:
+            curr_state, act, r, new_state, d = transition
+            current_state.append(curr_state)
+            action.append(act)
+            reward.append(r)
+            new_states.append(new_state)
+            done.append(d)
+
+        return np.array(current_state), action, reward, np.array(new_states), done
+
+    def sample_element_pri(self, batch_size):
+        # Prioritized DQN
+        # 根据td_error排序，求出索引index, 从小到大
+        index = np.argsort(np.array(self.buffer_td).flatten()).tolist()
+        # buffer 按index排序
+        buffer_sort = self.buffer
+        if len(index) != 0 and len(buffer_sort) != 0:
+            for i in range(len(self.buffer)):
+                buffer_sort[i] = self.buffer[index[i]]
+        prioritization = int(batch_size * self.alpha)  # self.alpha = 0.6
+        batch_prioritized = []
+        for i in range(prioritization):
+            # 反向添加，从大到小
+            batch_prioritized.append(buffer_sort[-i - 1])
+        mini_batch = random.sample(self.buffer, batch_size - prioritization)
+        td = self.buffer_td
+        # 最训练使用数据batch= batch_prioritized(按td_error从大到小)+mini_batch(随机抽取)
+        batch = batch_prioritized + mini_batch
+        current_state, action, reward, new_states, done, td_error = [], [], [], [], [], []
+
+        for transition in batch:
+            curr_state, act, r, new_state, d = transition
+            current_state.append(curr_state)
+            action.append([act])
+            reward.append([r])
+            new_states.append(new_state)
+            done.append([d])
+
+        return np.array(current_state), action, reward, np.array(new_states), done
+
+    def sample_all(self):
+        # for pre-train
+        current_state = [transition[0] for transition in self.buffer]
+        action = [transition[0] for transition in self.buffer]
+
+        return current_state, action
+
+    def size(self):
+        return len(self.buffer)
diff --git a/DQN_mulit_tensorflow_2/second_model/keras_metadata.pb b/DQN_mulit_tensorflow_2/second_model/keras_metadata.pb
new file mode 100644
index 0000000..f3af04e
--- /dev/null
+++ b/DQN_mulit_tensorflow_2/second_model/keras_metadata.pb
@@ -0,0 +1,12 @@
+
+�Broot"_tf_keras_sequential*�B{"name": "sequential", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 18, 11, 11]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_input"}}, {"class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 18, 11, 11]}, "dtype": "float32", "filters": 256, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_first", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": 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null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 64, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "shared_object_id": 17, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 18, 11, 11]}, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 18, 11, 11]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": 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diff --git a/DQN_mulit_tensorflow_2/second_model/saved_model.pb b/DQN_mulit_tensorflow_2/second_model/saved_model.pb
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index 0000000..5390aa3
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diff --git a/DQN_mulit_tensorflow_2/second_model/variables/variables.data-00000-of-00001 b/DQN_mulit_tensorflow_2/second_model/variables/variables.data-00000-of-00001
new file mode 100644
index 0000000..9d336c9
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diff --git a/DQN_mulit_tensorflow_2/second_model/variables/variables.index b/DQN_mulit_tensorflow_2/second_model/variables/variables.index
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diff --git a/DQN_mulit_tensorflow_2/utility.py b/DQN_mulit_tensorflow_2/utility.py
new file mode 100644
index 0000000..59d5cf4
--- /dev/null
+++ b/DQN_mulit_tensorflow_2/utility.py
@@ -0,0 +1,534 @@
+import numpy as np
+
+
+def reward_shaping(current_state, new_state, action, result, action_list):
+    r_win = 0
+    r_lose = -1
+
+    r_wood = 0.01
+    r_powerup = 0.05
+    r_kick = 0.02
+
+    # r_lay_bomb = -0.005
+    r_lay_bomb = 0.001
+    r_lay_bomb_near_enemy = 0.2
+    r_attack_teammate = -0.1
+    r_get_away_from_bomb = 0.005
+    r_get_close_to_bomb = -0.01
+
+    r_avoid = 0.001
+    r_move = 0.001
+    r_stay = -0.003
+    r_move_towards_wood = -0.01
+    r_move_loop = -0.005
+    r_dead_end = -0.1
+    r_ignore_penalty = -0.0015
+
+    reward = 0
+
+    X = current_state["position"][0]
+    Y = current_state["position"][1]
+    new_X = new_state["position"][0]
+    new_Y = new_state["position"][1]
+
+    # 左下情况
+    enemies = [11, 12, 13]
+    teammate = [10]
+
+    current_grids = []
+    if X - 1 >= 0:
+        current_grids.append(current_state["board"][X - 1][Y])
+    if X + 1 <= 10:
+        current_grids.append(current_state["board"][X + 1][Y])
+    if Y - 1 >= 0:
+        current_grids.append(current_state["board"][X][Y - 1])
+    if Y + 1 <= 10:
+        current_grids.append(current_state["board"][X][Y + 1])
+
+
+    if result == 1:
+        reward += r_win
+
+    # 检查是否踢了炸弹
+    if current_state["can_kick"] is True and new_state["can_kick"] is False:
+        reward += r_kick
+
+    if action == 0:
+        reward = check_avoid_flame(reward, r_avoid, current_grids)
+        reward = check_corner_bomb(current_state, X, Y, reward, r_avoid, r_stay, current_grids)
+        reward = check_in_flame(current_state, new_state, reward, r_lose, X, Y, new_X, new_Y)
+        reward = check_and_away_from_bomb(current_state, X, Y, new_X, new_Y, reward, r_get_away_from_bomb,
+                                          r_get_close_to_bomb, action_list)
+        return reward
+
+    # 移动reward
+    if action == 1:
+        # 检查是否撞墙
+        if current_state["position"] == new_state["position"]:
+            reward += r_move_towards_wood
+        else:
+            reward += r_move
+            reward = check_dead_end(new_state, new_X, new_Y, action_list, reward, r_dead_end)
+            reward = check_wood_and_power(current_state, new_X, new_Y, action_list, current_grids, reward,
+                                          r_ignore_penalty)
+        reward = check_move_loop(action_list, reward, r_move_loop)
+        reward = check_power_up(new_X, new_Y, current_state, reward, r_powerup)
+        reward = check_in_flame(current_state, new_state, reward, r_lose, X, Y, new_X, new_Y)
+        reward = check_and_away_from_bomb(current_state, X, Y, new_X, new_Y, reward, r_get_away_from_bomb,
+                                          r_get_close_to_bomb, action_list)
+        return reward
+
+    if action == 2:
+        # 检查是否撞墙
+        if current_state["position"] == new_state["position"]:
+            reward += r_move_towards_wood
+        else:
+            reward += r_move
+            reward = check_dead_end(new_state, new_X, new_Y, action_list, reward, r_dead_end)
+            reward = check_wood_and_power(current_state, new_X, new_Y, action_list, current_grids, reward,
+                                          r_ignore_penalty)
+        reward = check_move_loop(action_list, reward, r_move_loop)
+        reward = check_power_up(new_X, new_Y, current_state, reward, r_powerup)
+        reward = check_in_flame(current_state, new_state, reward, r_lose, X, Y, new_X, new_Y)
+        reward = check_and_away_from_bomb(current_state, X, Y, new_X, new_Y, reward, r_get_away_from_bomb,
+                                          r_get_close_to_bomb, action_list)
+        return reward
+
+    if action == 3:
+        # 检查是否撞墙
+        if current_state["position"] == new_state["position"]:
+            reward += r_move_towards_wood
+        else:
+            reward += r_move
+            reward = check_dead_end(new_state, new_X, new_Y, action_list, reward, r_dead_end)
+            reward = check_wood_and_power(current_state, new_X, new_Y, action_list, current_grids, reward,
+                                          r_ignore_penalty)
+        reward = check_move_loop(action_list, reward, r_move_loop)
+        reward = check_power_up(new_X, new_Y, current_state, reward, r_powerup)
+        reward = check_in_flame(current_state, new_state, reward, r_lose, X, Y, new_X, new_Y)
+        reward = check_and_away_from_bomb(current_state, X, Y, new_X, new_Y, reward, r_get_away_from_bomb,
+                                          r_get_close_to_bomb, action_list)
+        return reward
+
+    if action == 4:
+        # 检查是否撞墙
+        if current_state["position"] == new_state["position"]:
+            reward += r_move_towards_wood
+        else:
+            reward += r_move
+            reward = check_dead_end(new_state, new_X, new_Y, action_list, reward, r_dead_end)
+            reward = check_wood_and_power(current_state, new_X, new_Y, action_list, current_grids, reward,
+                                          r_ignore_penalty)
+        reward = check_move_loop(action_list, reward, r_move_loop)
+        reward = check_power_up(new_X, new_Y, current_state, reward, r_powerup)
+        reward = check_in_flame(current_state, new_state, reward, r_lose, X, Y, new_X, new_Y)
+        reward = check_and_away_from_bomb(current_state, X, Y, new_X, new_Y, reward, r_get_away_from_bomb,
+                                          r_get_close_to_bomb, action_list)
+        return reward
+
+    # 放炸弹reward，包括检查wood， 敌人等
+    if action == 5:
+
+        reward = check_in_flame(current_state, new_state, reward, r_lose, X, Y, new_X, new_Y)
+        reward = check_and_away_from_bomb(current_state, X, Y, new_X, new_Y, reward, r_get_away_from_bomb,
+                                          r_get_close_to_bomb, action_list)
+        if current_state["ammo"] != 0:
+            reward += r_lay_bomb
+            reward = check_bomb_reward(current_state, X, Y, reward, r_wood, r_lay_bomb_near_enemy, r_attack_teammate,
+                                       enemies, teammate)
+        else:
+            #reward += (2 * r_move_towards_wood)
+            reward += -0.001
+
+        return reward
+
+
+def check_bomb_reward(current_state, X, Y, reward, r_wood, r_lay_bomb_near_enemy, r_attack_teammate, enemies, teammate):
+    blast_strength = current_state["blast_strength"]
+    # 判断炸弹左方是否有墙
+    for strength in range(1, blast_strength):
+        # 检查是否超出地图边界
+        if Y - strength < 0:
+            break
+        # 检查是否有wood
+        elif current_state["board"][X][Y - strength] == 2:
+            reward += r_wood
+            break
+        # 如果是rigid，则break
+        elif current_state["board"][X][Y - strength] == 1:
+            break
+        # 如果爆炸范围内有敌人，获得reward
+        elif current_state["board"][X][Y - strength] in enemies:
+            reward += r_lay_bomb_near_enemy
+        elif current_state["board"][X][Y - strength] in teammate:
+            reward += r_attack_teammate
+
+    # 判断炸弹右方是否有墙
+    for strength in range(1, blast_strength):
+        # 检查是否超出地图边界
+        if Y + strength > 10:
+            break
+        # 检查是否有wood
+        elif current_state["board"][X][Y + strength] == 2:
+            reward += r_wood
+            break
+        # 如果是rigid，则break
+        elif current_state["board"][X][Y + strength] == 1:
+            break
+        # 如果爆炸范围内有敌人，获得reward
+        elif current_state["board"][X][Y + strength] in enemies:
+            reward += r_lay_bomb_near_enemy
+        elif current_state["board"][X][Y + strength] in teammate:
+            reward += r_attack_teammate
+
+    # 判断炸弹上方是否有墙
+    for strength in range(1, blast_strength):
+        # 检查是否超出地图边界
+        if X - strength < 0:
+            break
+        # 检查是否有wood
+        elif current_state["board"][X - strength][Y] == 2:
+            reward += r_wood
+            break
+        # 如果是rigid，则break
+        elif current_state["board"][X - strength][Y] == 1:
+            break
+        # 如果爆炸范围内有敌人，获得reward
+        elif current_state["board"][X - strength][Y] in enemies:
+            reward += r_lay_bomb_near_enemy
+        elif current_state["board"][X - strength][Y] in teammate:
+            reward += r_attack_teammate
+
+    # 判断炸弹下方是否有墙
+    for strength in range(1, blast_strength):
+        # 检查是否超出地图边界
+        if X + strength > 10:
+            break
+        # 检查是否有wood
+        elif current_state["board"][X + strength][Y] == 2:
+            reward += r_wood
+            break
+        # 如果是rigid，则break
+        elif current_state["board"][X + strength][Y] == 1:
+            break
+        # 如果爆炸范围内有敌人，获得reward
+        elif current_state["board"][X + strength][Y] in enemies:
+            reward += r_lay_bomb_near_enemy
+        elif current_state["board"][X + strength][Y] in teammate:
+            reward += r_attack_teammate
+    return reward
+
+
+def check_in_flame(current_state, new_state, reward, r_lose, X, Y, new_X, new_Y):
+    # 若agent与火焰位置重叠，则死亡，返回reward
+
+    if current_state["flame_life"][X][Y] == 0 and new_state["flame_life"][new_X][new_Y] != 0:
+        reward += r_lose
+    return reward
+
+
+def check_power_up(new_X, new_Y, current_state, reward, r_power_up):
+    if current_state["board"][new_X][new_Y] in [6, 7, 8]:
+        reward += r_power_up
+
+    return reward
+
+
+def check_corner_bomb(current_state, X, Y, reward, r_avoid, r_stay, current_grids):
+    # action 0 来躲避左上bomb
+    find_bomb = False
+    if X - 1 >= 0 and Y - 1 >= 0 and current_state["board"][X - 1][Y - 1] == 3:
+        reward += r_avoid
+        find_bomb = True
+    if X - 1 >= 0 and Y - 2 >= 0 and current_state["board"][X - 1][Y - 2] == 3:
+        reward += r_avoid
+        find_bomb = True
+        # 右上
+    if X - 1 >= 0 and Y + 1 <= 10 and current_state["board"][X - 1][Y + 1] == 3:
+        reward += r_avoid
+        find_bomb = True
+    if X - 1 >= 0 and Y + 2 <= 10 and current_state["board"][X - 1][Y + 2] == 3:
+        reward += r_avoid
+        find_bomb = True
+    # 左下
+    if X + 1 <= 10 and Y - 1 >= 0 and current_state["board"][X + 1][Y - 1] == 3:
+        reward += r_avoid
+        find_bomb = True
+    if X + 2 <= 10 and Y - 1 >= 0 and current_state["board"][X + 2][Y - 1] == 3:
+        reward += r_avoid
+        find_bomb = True
+    # 右下
+    if X + 1 <= 10 and Y + 1 <= 10 and current_state["board"][X + 1][Y + 1] == 3:
+        reward += r_avoid
+        find_bomb = True
+    if X + 1 <= 10 and Y + 2 <= 10 and current_state["board"][X + 1][Y + 2] == 3:
+        reward += r_avoid
+        find_bomb = True
+    if not find_bomb and 3 not in current_grids:
+        reward += r_stay
+
+    return reward
+
+
+def check_and_away_from_bomb(current_state, X, Y, new_X, new_Y, reward, r_get_away_from_bomb, r_get_close_to_bomb,
+                             action_list):
+    # 远离上下左右四个方向的炸弹
+    if action_list[2] == 5 and (X != new_X or Y != new_Y):
+        reward += r_get_away_from_bomb
+    elif action_list[2] == 5 and (X == new_X and Y == new_Y):
+        reward += 2 * r_get_close_to_bomb
+    # 上
+    if X - 1 >= 0 and current_state["board"][X - 1][Y] == 3 and (abs((X - 1) - new_X) + abs(Y - new_Y)) > 1:
+        reward += r_get_away_from_bomb
+    if X - 1 >= 0 and current_state["board"][X - 1][Y] == 3 and (abs((X - 1) - new_X) + abs(Y - new_Y)) == 1:
+        reward += 2 * r_get_close_to_bomb
+    if X - 2 >= 0 and current_state["board"][X - 2][Y] == 3 and (abs((X - 2) - new_X) + abs(Y - new_Y)) > 2 and \
+            current_state["board"][X - 1][Y] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif X - 2 >= 0 and current_state["board"][X - 2][Y] == 3 and (abs((X - 2) - new_X) + abs(Y - new_Y)) <= 2 and \
+            current_state["board"][X - 1][Y] not in [1, 2]:
+        reward += r_get_close_to_bomb
+    if X - 3 >= 0 and current_state["board"][X - 3][Y] == 3 and (abs((X - 3) - new_X) + abs(Y - new_Y)) > 3 and \
+            current_state["board"][X - 1][Y] not in [1, 2] and current_state["board"][X - 2][Y] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif X - 3 >= 0 and current_state["board"][X - 3][Y] == 3 and (abs((X - 3) - new_X) + abs(Y - new_Y)) <= 3 and \
+            current_state["board"][X - 1][Y] not in [1, 2] and current_state["board"][X - 2][Y] not in [1, 2]:
+        reward += r_get_close_to_bomb
+    # 下
+    if X + 1 <= 10 and current_state["board"][X + 1][Y] == 3 and (abs((X + 1) - new_X) + abs(Y - new_Y)) > 1:
+        reward += r_get_away_from_bomb
+    if X + 1 <= 10 and current_state["board"][X + 1][Y] == 3 and (abs((X + 1) - new_X) + abs(Y - new_Y)) == 1:
+        reward += 2 * r_get_close_to_bomb
+    if X + 2 <= 10 and current_state["board"][X + 2][Y] == 3 and (abs((X + 2) - new_X) + abs(Y - new_Y)) > 2 and \
+            current_state["board"][X + 1][Y] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif X + 2 <= 10 and current_state["board"][X + 2][Y] == 3 and (abs((X + 2) - new_X) + abs(Y - new_Y)) <= 2 and \
+            current_state["board"][X + 1][Y] not in [1, 2]:
+        reward += r_get_close_to_bomb
+    if X + 3 <= 10 and current_state["board"][X + 3][Y] == 3 and (abs((X + 3) - new_X) + abs(Y - new_Y)) > 3 and \
+            current_state["board"][X + 1][Y] not in [1, 2] and current_state["board"][X + 2][Y] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif X + 3 <= 10 and current_state["board"][X + 3][Y] == 3 and (abs((X + 3) - new_X) + abs(Y - new_Y)) <= 3 and \
+            current_state["board"][X + 1][Y] not in [1, 2] and current_state["board"][X + 2][Y] not in [1, 2]:
+        reward += r_get_close_to_bomb
+
+    # 左
+    if Y - 1 >= 0 and current_state["board"][X][Y - 1] == 3 and (abs(X - new_X) + abs((Y - 1) - new_Y)) > 1:
+        reward += r_get_away_from_bomb
+    if Y - 1 >= 0 and current_state["board"][X][Y - 1] == 3 and (abs(X - new_X) + abs((Y - 1) - new_Y)) == 1:
+        reward += 2 * r_get_close_to_bomb
+    if Y - 2 >= 0 and current_state["board"][X][Y - 2] == 3 and (abs(X - new_X) + abs((Y - 2) - new_Y)) > 2 and \
+            current_state["board"][X][Y - 1] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif Y - 2 >= 0 and current_state["board"][X][Y - 2] == 3 and (abs(X - new_X) + abs((Y - 2) - new_Y)) <= 2 and \
+            current_state["board"][X][Y - 1] not in [1, 2]:
+        reward += r_get_close_to_bomb
+    if Y - 3 >= 0 and current_state["board"][X][Y - 3] == 3 and (abs(X - new_X) + abs((Y - 3) - new_Y)) > 3 and \
+            current_state["board"][X][Y - 1] not in [1, 2] and current_state["board"][X][Y - 2] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif Y - 3 >= 0 and current_state["board"][X][Y - 3] == 3 and (abs(X - new_X) + abs((Y - 3) - new_Y)) <= 3 and \
+            current_state["board"][X][Y - 1] not in [1, 2] and current_state["board"][X][Y - 2] not in [1, 2]:
+        reward += r_get_close_to_bomb
+
+    # 右
+    if Y + 1 <= 10 and current_state["board"][X][Y + 1] == 3 and (abs(X - new_X) + abs((Y + 1) - new_Y)) > 1:
+        reward += r_get_away_from_bomb
+    if Y + 1 <= 10 and current_state["board"][X][Y + 1] == 3 and (abs(X - new_X) + abs((Y + 1) - new_Y)) == 1:
+        reward += 2 * r_get_close_to_bomb
+    if Y + 2 <= 10 and current_state["board"][X][Y + 2] == 3 and (abs(X - new_X) + abs((Y + 2) - new_Y)) > 2 and \
+            current_state["board"][X][Y + 1] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif Y + 2 <= 10 and current_state["board"][X][Y + 2] == 3 and (abs(X - new_X) + abs((Y + 2) - new_Y)) <= 2 and \
+            current_state["board"][X][Y + 1] not in [1, 2]:
+        reward += r_get_close_to_bomb
+    if Y + 3 <= 10 and current_state["board"][X][Y + 3] == 3 and (abs(X - new_X) + abs((Y + 3) - new_Y)) > 3 and \
+            current_state["board"][X][Y + 1] not in [1, 2] and current_state["board"][X][Y + 2] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif Y + 3 <= 10 and current_state["board"][X][Y + 3] == 3 and (abs(X - new_X) + abs((Y + 3) - new_Y)) <= 3 and \
+            current_state["board"][X][Y + 1] not in [1, 2] and current_state["board"][X][Y + 2] not in [1, 2]:
+        reward += r_get_close_to_bomb
+
+    # 检查四角方向 左上
+    if X - 1 >= 0 and Y - 1 >= 0 and current_state["board"][X - 1][Y - 1] == 3 and (abs((X - 1) - new_X)) + abs(
+            (Y - 1) - new_Y) > 2:
+        reward += r_get_away_from_bomb
+    elif X - 1 >= 0 and Y - 1 >= 0 and current_state["board"][X - 1][Y - 1] == 3 and (abs((X - 1) - new_X)) + abs(
+            (Y - 1) - new_Y) < 2:
+        reward += r_get_close_to_bomb
+    # 左下
+    if X + 1 <= 10 and Y - 1 >= 0 and current_state["board"][X + 1][Y - 1] == 3 and (abs((X + 1) - new_X)) + abs(
+            (Y - 1) - new_Y) > 2:
+        reward += r_get_away_from_bomb
+    elif X + 1 <= 10 and Y - 1 >= 0 and current_state["board"][X + 1][Y - 1] == 3 and (abs((X + 1) - new_X)) + abs(
+            (Y - 1) - new_Y) < 2:
+        reward += r_get_close_to_bomb
+    # 右上
+    if X - 1 >= 0 and Y + 1 <= 10 and current_state["board"][X - 1][Y + 1] == 3 and (abs((X - 1) - new_X)) + abs(
+            (Y + 1) - new_Y) > 2:
+        reward += r_get_away_from_bomb
+    elif X - 1 >= 0 and Y + 1 <= 10 and current_state["board"][X - 1][Y + 1] == 3 and (abs((X - 1) - new_X)) + abs(
+            (Y + 1) - new_Y) < 2:
+        reward += r_get_close_to_bomb
+
+    # 右下
+    if X + 1 <= 10 and Y + 1 <= 10 and current_state["board"][X + 1][Y + 1] == 3 and (abs((X + 1) - new_X)) + abs(
+            (Y + 1) - new_Y) > 2:
+        reward += r_get_away_from_bomb
+    elif X + 1 <= 10 and Y + 1 <= 10 and current_state["board"][X + 1][Y + 1] == 3 and (abs((X + 1) - new_X)) + abs(
+            (Y - 1) + new_Y) < 2:
+        reward += r_get_close_to_bomb
+
+    return reward
+
+
+def check_move_loop(action_list, reward, r_move_loop):
+    check_list = [[1, 2, 1, 2],
+                  [2, 1, 2, 1],
+                  [3, 4, 3, 4],
+                  [4, 3, 4, 3],
+                  [1, 2, 3, 4],
+                  [2, 1, 3, 4],
+                  [3, 4, 1, 2],
+                  [3, 4, 2, 1],
+                  [1, 2, 4, 3],
+                  [2, 1, 4, 3],
+                  [4, 3, 1, 2],
+                  [4, 3, 2, 1]]
+    # if action_list[0] == action_list[2] and action_list[1] == action_list[3] and (
+    #         action_list[0] and action_list[1] and action_list[2] and action_list[3] in [1, 2, 3, 4]) and \
+    #         action_list[1] != action_list[0] and (action_list not in check_list):
+    if action_list in check_list:
+        reward += r_move_loop
+    return reward
+
+
+def check_dead_end(new_state, new_X, new_Y, action_list, reward, r_dead_end):
+    if action_list[2] == 5 and action_list[3] == 1:
+        if (new_Y - 1 < 0 or new_state["board"][new_X][new_Y - 1] in [1, 2, 3]) and \
+                (new_Y + 1 > 10 or new_state["board"][new_X][new_Y + 1] in [1, 2, 3]) and \
+                (new_X - 1 < 0 or new_state["board"][new_X - 1][new_Y] in [1, 2, 3]):
+            reward += r_dead_end
+            return reward
+    elif action_list[2] == 5 and action_list[3] == 2:
+        if (new_Y - 1 < 0 or new_state["board"][new_X][new_Y - 1] in [1, 2, 3]) and \
+                (new_Y + 1 > 10 or new_state["board"][new_X][new_Y + 1] in [1, 2, 3]) and \
+                (new_X + 1 > 10 or new_state["board"][new_X + 1][new_Y] in [1, 2, 3]):
+            reward += r_dead_end
+            return reward
+    elif action_list[2] == 5 and action_list[3] == 3:
+        if (new_X - 1 < 0 or new_state["board"][new_X - 1][new_Y] in [1, 2, 3]) and \
+                (new_Y - 1 < 0 or new_state["board"][new_X][new_Y - 1] in [1, 2, 3]) and \
+                (new_X + 1 > 10 or new_state["board"][new_X + 1][new_Y] in [1, 2, 3]):
+            reward += r_dead_end
+            return reward
+    elif action_list[2] == 5 and action_list[3] == 4:
+        if (new_X - 1 < 0 or new_state["board"][new_X - 1][new_Y] in [1, 2, 3]) and \
+                (new_Y + 1 > 10 or new_state["board"][new_X][new_Y + 1] in [1, 2, 3]) and \
+                (new_X + 1 > 10 or new_state["board"][new_X + 1][new_Y] in [1, 2, 3]):
+            reward += r_dead_end
+            return reward
+
+    return reward
+
+
+def check_avoid_flame(reward, r_avoid, current_grids):
+    if 4 in current_grids and all((grid in [1, 2, 3, 4]) for grid in current_grids):
+        reward += r_avoid
+    return reward
+
+
+def check_wood_and_power(current_state, new_X, new_Y, action_list, current_grids, reward, r_ignore_penalty):
+    power = False
+    # 先检查power 更重要
+    if ((6 or 7 or 8) in current_grids) and current_state["board"][new_X][new_Y] not in [6, 7, 8] and (
+            5 not in action_list) and \
+            current_state["ammo"] != 0:
+        reward += r_ignore_penalty
+        power = True
+    # if power is False and (2 in current_grids) and (5 not in action_list) and current_state["ammo"] != 0:
+    #     reward += r_ignore_penalty
+
+    return reward
+
+
+def featurize2D(states, partially_obs=True):
+    # 共18个矩阵
+    X = states["position"][0]
+    Y = states["position"][1]
+    shape = (11, 11)
+    # for 1v1
+    #shape = (8, 8)
+
+    # path, rigid, wood, bomb, flame, fog, power_up, agent1, agent2, agent3, agent4
+    def get_partially_obs(states, X, Y):
+        # board = np.zeros(shape)
+        board = np.full(shape, 5)
+        for x in range(10):
+            for y in range(10):
+                if X - 4 <= x <= X + 4 and Y - 4 <= y <= Y + 4:
+                    board[x][y] = states["board"][x][y]
+        states["board"] = board
+        return states
+
+    def get_matrix(board, key):
+        res = board[key]
+        return res.reshape(shape).astype(np.float64)
+
+    def get_map(board, item):
+        map = np.zeros(shape)
+        map[board == item] = 1
+        return map
+
+    if partially_obs:
+        states = get_partially_obs(states, X, Y)
+
+    board = get_matrix(states, "board")
+
+    path = get_map(board, 0)
+    rigid = get_map(board, 1)
+    wood = get_map(board, 2)
+    bomb = get_map(board, 3)
+    flame = get_map(board, 4)
+    # fog = get_map(board, 5)
+    fog = np.zeros(shape)
+    agent1 = get_map(board, 10)
+    agent2 = get_map(board, 11)
+    agent3 = get_map(board, 12)
+    agent4 = get_map(board, 13)
+
+    power_up = []
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 6 or num == 7 or num == 8:
+                new_row.append(1)
+            else:
+                new_row.append(0.0)
+        power_up.append(new_row)
+
+    bomb_blast_strength = get_matrix(states, 'bomb_blast_strength')
+    bomb_life = get_matrix(states, 'bomb_life')
+    bomb_moving_direction = get_matrix(states, 'bomb_moving_direction')
+    flame_life = get_matrix(states, 'flame_life')
+
+    ammo_2D, blast_strength_2D, can_kick_2D = rebuild_1D_element(states)
+
+    feature2D = [path, rigid, wood, bomb, flame, fog, power_up, agent1, agent2, agent3, agent4, bomb_blast_strength,
+                 bomb_life, bomb_moving_direction, flame_life, ammo_2D, blast_strength_2D, can_kick_2D]
+
+    return np.array(feature2D)
+
+
+def rebuild_1D_element(states):
+    shape = (11,11)
+    #shape = (8, 8)
+    ammo = states["ammo"]
+    ammo_2D = np.full(shape, ammo)
+
+    blast_strength = states["blast_strength"]
+    blast_strength_2D = np.full(shape, blast_strength)
+
+    can_kick = states["can_kick"]
+    can_kick_2D = np.full(shape, int(can_kick))
+
+    return ammo_2D, blast_strength_2D, can_kick_2D
diff --git a/DQN_multi_tensorflow/DQNAgent.py b/DQN_multi_tensorflow/DQNAgent.py
new file mode 100644
index 0000000..444f46a
--- /dev/null
+++ b/DQN_multi_tensorflow/DQNAgent.py
@@ -0,0 +1,156 @@
+from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
+from keras import Sequential
+from tensorflow.keras.optimizers import Adam
+from pommerman.agents import BaseAgent
+from pommerman.agents.random_agent import RandomAgent
+from pommerman import characters
+
+from gym.spaces import Discrete
+from utility import reward_shaping
+from DQN_new import constants
+from replay_memory import replay_Memory
+import numpy as np
+import os
+import tensorflow as tf
+
+
+class DQNAgent(BaseAgent):
+    """DQN second try with keras"""
+
+    def __init__(self, character=characters.Bomber):
+        super(DQNAgent, self).__init__(character)
+        self.baseAgent = RandomAgent()
+
+        self.training_model = self.new_model()
+        self.trained_model = self.new_model()
+        # self.trained_model.set_weights(self.training_model.get_weights())
+
+        self.epsilon = constants.epsilon
+        self.min_epsilon = constants.MIN_EPSILON
+        self.eps_decay = constants.EPSILON_DECAY
+        self.buffer = replay_Memory(constants.MAX_BUFFER_SIZE)
+        self.target_update_counter = 0
+
+    def new_model(self):
+
+        model = Sequential()
+        input_shape = (constants.MINIBATCH_SIZE, 14, 11, 11)
+        model.add(Conv2D(256, 3, input_shape=input_shape[1:], activation="relu"))
+        # print(model.output_shape)
+        model.add(MaxPooling2D(pool_size=(3, 3), data_format="channels_first"))
+        model.add(Dropout(0.2))
+
+        model.add(Conv2D(256, 2, activation="relu"))
+        model.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))
+        model.add(Dropout(0.2))
+
+        model.add(Flatten())
+        model.add(Dense(64))
+
+        model.add(Dense(6, activation='softmax'))
+        model.compile(loss="mse", optimizer=Adam(learning_rate=0.001), metrics=['accuracy'])
+
+        return model
+
+    def act(self, obs, action_space):
+        return self.baseAgent.act(obs, Discrete(6))
+
+    def train(self, done, numOfEpisode):
+
+        if self.buffer.size() < constants.MIN_REPLAY_MEMORY_SIZE:
+            return
+
+        # if numOfEpisode == 0:
+        #     self.training_model.load_weights('./checkpoints/my_checkpoint')
+        #     self.trained_model.load_weights('./checkpoints/my_checkpoint')
+
+        # if numOfEpisode % 999 == 0:
+        #     checkpoint_path = "/checkpoints/training1/cp.ckpt"
+        #     checkpoint_dir = os.path.dirname(checkpoint_path)
+        #
+        #     # 检查点重用
+        #     cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
+        #                                                      save_weights_only=True,
+        #                                                      verbose=1)
+        # # 完成一次训练后存档参数
+        # if numOfEpisode == 4999:
+        #     self.training_model.save_weights('/checkpoints/my_checkpoint')
+
+        # 取样
+        mini_batch = self.buffer.sample(constants.MINIBATCH_SIZE)
+
+        computed_reward = reward_shaping(mini_batch)
+
+        # 在样品中取 current_states, 从模型中获取Q值
+        current_states = np.array([transition[0] for transition in mini_batch])
+        current_qs_list = self.training_model.predict(self.get_state(current_states))
+
+        # 将计算后的reward赋值到mini_batch中
+        for index in range(0, constants.MINIBATCH_SIZE):
+            mini_batch[index][2] = computed_reward[index]
+
+        # 在样品中取 next_state, 从网络中获取Q值
+        next_state = np.array([transition[3] for transition in mini_batch])
+        future_qs_list = self.trained_model.predict(self.get_state(next_state))
+
+        # X为state，Y为所预测的action
+        X = []
+        Y = []
+
+        for index, (current_state, action, reward, new_current_state, done) in enumerate(mini_batch):
+            # 如果 done， 则不会有future_Q
+            if not done:
+                # 更新Q值
+                max_future_q = np.max(future_qs_list[index])
+                next_q = reward + constants.DISCOUNT * max_future_q
+            else:
+                next_q = reward
+
+            # 取新q值
+            # next_q = (constants.DISCOUNT * max_future_q + reward) * ~done + done * reward
+
+            # 在给定的states下更新Q值
+            current_qs = current_qs_list[index]
+            current_qs[action] = next_q
+
+            # 添加训练数据
+            X.append(current_state)
+            # X.append(tf.reshape(current_state,(-1,12,8,8)))
+            Y.append(current_qs)
+
+        # 提取出local中的state
+        X = self.get_state(X)
+
+        # 开始训练
+        # 使用tensorflow的dataset接口,但没什么用
+        # X = tf.reshape(X, (-1, 12, 8, 8))
+        # train_dataset = tf.data.Dataset.from_tensor_slices((X, Y))
+        # self.training_model.fit(train_dataset, verbose=0, shuffle=False)
+
+        self.training_model.fit(np.array(X), np.array(Y), epochs=4, batch_size=constants.MINIBATCH_SIZE, verbose=0,
+                                shuffle=False)
+
+        # 更新网络更新计数器
+        if done:
+            self.target_update_counter += 1
+
+        # 网络更新计数器达到上限，更新网络
+        if self.target_update_counter > constants.UPDATE_EVERY:
+            self.trained_model.set_weights(self.training_model.get_weights())
+        self.target_update_counter = 0
+
+    def get_q_value(self, state):
+
+        state_reshape = tf.reshape(state, (-1, 14, 11, 11))
+        return self.training_model.predict_on_batch(state_reshape)
+
+        # epsilon衰减
+
+    def get_state(self, states):
+        new_state = [states[index]["local"] for index in range(0, constants.MINIBATCH_SIZE)]
+        # for index in range(0, constants.MINIBATCH_SIZE):
+        #     new_state.append(states[index]["local"])
+        return new_state
+
+    def epsilon_decay(self):
+        self.epsilon = self.epsilon * self.eps_decay if self.epsilon > self.min_epsilon else self.epsilon
diff --git a/DQN_multi_tensorflow/constants.py b/DQN_multi_tensorflow/constants.py
new file mode 100644
index 0000000..401471d
--- /dev/null
+++ b/DQN_multi_tensorflow/constants.py
@@ -0,0 +1,19 @@
+MIN_REPLAY_MEMORY_SIZE = 1_000  # Minimum number of steps in a memory to start training
+MINIBATCH_SIZE = 128  # How many steps (samples) to use for training
+UPDATE_TARGET_EVERY = 10  # Terminal states (end of episodes)
+MAX_BUFFER_SIZE = 50_000
+DISCOUNT = 0.95
+
+# Environment settings
+EPISODES = 3000
+SHOW_EVERY = 1
+
+# Exploration settings
+epsilon = 1  # not a constant, going to be decayed
+EPSILON_DECAY = 0.99975
+MIN_EPSILON = 0.001
+
+#  Stats settings
+AGGREGATE_STATS_EVERY = 300  # episodes
+SHOW_PREVIEW = False
+
diff --git a/DQN_multi_tensorflow/main_multi.py b/DQN_multi_tensorflow/main_multi.py
new file mode 100644
index 0000000..1f6b0be
--- /dev/null
+++ b/DQN_multi_tensorflow/main_multi.py
@@ -0,0 +1,97 @@
+import random
+import tensorflow as tf
+import pommerman
+import numpy as np
+from pommerman import constants
+#from DQN_new import DQNAgent
+from DQNAgent import DQNAgent
+#from pommerman.agents.simple_agent import SimpleAgent
+from pommerman.agents import SimpleAgent
+import constants
+from utility import featurize2D,featurize
+
+
+def main():
+
+    agent1 = DQNAgent()
+    agent2 = SimpleAgent()
+    agent3 = SimpleAgent()
+    agent4 = SimpleAgent()
+
+    agent_list = [agent1, agent2, agent3, agent4]
+    env = pommerman.make('PommeRadioCompetition-v2', agent_list)
+
+    episode_rewards = []  # 记录平均reward
+    #action_n = env.action_space.n  # action空间
+
+    win = 0
+    total_game = 0
+    win_rate = 0
+
+    # devices = tf.config.experimental.list_physical_devices('GPU')
+    # tf.config.experimental.set_memory_growth(devices[0], True)
+
+    for episode in range(constants.EPISODES + 1):
+
+        current_state = env.reset()
+        # 将state 转化 1D array
+        state_feature = featurize(current_state[0])
+        episode_reward = 0
+
+        done = False
+        while not done:
+            if constants.epsilon > np.random.random():
+                # 获取动作
+                actions = env.act(current_state)
+                actions[0] = np.argmax(agent1.get_q_value(state_feature["local"])).tolist()
+                #actions[0] = random.randint(0, 5)
+            else:
+                # 随机动作
+                actions = env.act(current_state)
+                actions[0] = random.randint(0, 5)
+
+            new_state, reward, done, info = env.step(actions)
+            next_state_feature = featurize(new_state[0])
+            episode_reward += reward[0]
+
+            # 每一定局数显示游戏画面
+            # if constants.SHOW_PREVIEW and not episode % constants.SHOW_GAME:
+            env.render()
+
+            # 储存记忆
+            agent1.buffer.append([state_feature, actions[0], reward[0], next_state_feature, done])
+
+            # 学习!
+            agent1.train(done, episode+1)
+
+            # 更新state
+            current_state = new_state
+
+            if done:
+                break
+
+        if done:
+            episode_rewards.append(episode_reward)
+            total_game += 1
+            if 0 in info.get('winners', []):
+                win += 1
+
+        win_rate = win / total_game
+
+        if episode % constants.SHOW_EVERY == 0:
+            print("{} of 3000 episodes done, result: {}".format(episode + 1,
+                                                                'Win' if 0 in info.get('winners', []) else 'Lose'))
+            print("Average Episode Reward: {:.3f}, win_rate_last_1000_game:{:.2f}".format(np.mean(episode_rewards),
+                                                                                          win_rate))
+
+        if total_game >= 999:
+            win = 0
+            total_game = 0
+
+        agent1.epsilon_decay()
+
+    env.close()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/DQN_multi_tensorflow/replay_memory.py b/DQN_multi_tensorflow/replay_memory.py
new file mode 100644
index 0000000..904ef8c
--- /dev/null
+++ b/DQN_multi_tensorflow/replay_memory.py
@@ -0,0 +1,31 @@
+import random
+import collections
+
+
+class replay_Memory():
+    def __init__(self, MAX_BUFFER_SIZE):
+        self.buffer = collections.deque(maxlen=MAX_BUFFER_SIZE)
+
+    def append(self, transition):
+        self.buffer.append(transition)
+
+    # 无reward
+    def sample(self, batch):
+        mini_batch = random.sample(self.buffer, batch)
+        # curr_s, act, reward, next_s, done = [], [], [], [], []
+        #
+        # # transition: tuple
+        # for transition in mini_batch:
+        #     c_s, a, r, n_s, d = transition
+        #     curr_s.append(c_s)
+        #     act.append([a])
+        #     reward.append([r])
+        #     next_s.append(n_s)
+        #     done.append([d])
+
+        # 构建transition
+        return mini_batch
+
+
+    def size(self):
+        return len(self.buffer)
diff --git a/DQN_multi_tensorflow/test.py b/DQN_multi_tensorflow/test.py
new file mode 100644
index 0000000..181b69d
--- /dev/null
+++ b/DQN_multi_tensorflow/test.py
@@ -0,0 +1,3 @@
+import random
+for i in range(0,10):
+    print(i)
\ No newline at end of file
diff --git a/DQN_multi_tensorflow/utility.py b/DQN_multi_tensorflow/utility.py
new file mode 100644
index 0000000..4f01a8d
--- /dev/null
+++ b/DQN_multi_tensorflow/utility.py
@@ -0,0 +1,404 @@
+import numpy as np
+from pommerman import utility
+
+
+def featurize(states):
+    '''
+    Converts the states(dict) into list of 1D numpy arrays
+
+    Input:
+    - env: gym environment
+    - states: list[num_agents, dict(15)] for each agent
+    Output:
+    - feature: list[num_agents, 372]
+    '''
+
+    # length = len(env.featurize(states[0]).tolist())
+    # list = env.featurize(states[0]).tolist()
+    # states = states[0]
+    local = featurize2D(states)
+    """
+    board = states["board"].reshape(-1).astype(np.float32)
+    bomb_blast_strength = states["bomb_blast_strength"].reshape(-1).astype(np.float32)
+    bomb_life = states["bomb_life"].reshape(-1).astype(np.float32)
+    bomb_moving_direction = states["bomb_moving_direction"].reshape(-1).astype(np.float32)
+    flame_life = states["flame_life"].reshape(-1).astype(np.float32)
+    local.append(board.tolist())
+    local.append(bomb_blast_strength.tolist())
+    local.append(bomb_life.tolist())
+    local.append(bomb_moving_direction.tolist())
+    local.append(flame_life.tolist())
+    """
+    feature = {'local': local}
+    additional = []
+    position = utility.make_np_float(states["position"])
+    ammo = utility.make_np_float([states["ammo"]])  # fff
+    blast_strength = utility.make_np_float([states["blast_strength"]])
+    can_kick = utility.make_np_float([states["can_kick"]])
+    teammate = utility.make_np_float([states["teammate"].value])
+    enemies = utility.make_np_float([e.value for e in states["enemies"]])
+    # print(position, ammo, blast_strength, can_kick, teammate, enemies)
+    """
+    additional.append(position.tolist())
+    additional.append(ammo.tolist())
+    additional.append(blast_strength.tolist())
+    additional.append(can_kick.tolist())
+    additional.append(teammate.tolist())
+    additional.append(enemies.tolist())
+    """
+    # print(additional)
+    # position占两个数，所以你要取ammo的话就要取additional[2]
+    additional = np.concatenate(
+        (position, ammo,
+         blast_strength, can_kick, teammate, enemies))
+
+    feature['additional'] = additional.tolist()
+    return feature
+
+
+def reward_shaping(minibatch):
+    mini_batch = minibatch
+
+    statesl, statesa, actions, rewards, next_statesl, next_statesa, done = [], [], [], [], [], [], []
+    # epistep = []
+
+    for transition in mini_batch:  # transition: tuple
+        state, additions, re, new_state, done_mask = transition
+        statesl.append(state['local'])
+        statesa.append(state['additional'])
+        actions.append([additions])
+        rewards.append([re])
+        next_statesl.append(new_state['local'])
+        next_statesa.append(new_state['additional'])
+        done.append([done_mask])
+    """
+    sl_lst = np.array(sl_lst)
+    sa_lst = np.array(sa_lst)
+    a_lst = np.array(a_lst)
+    r_lst = np.array(r_lst)
+    sl_prime_list = np.array(sl_prime_list)
+    sa_prime_list = np.array(sa_prime_list)
+    done_mask_list = np.array(done_mask_list)
+    """
+    computed_reward = []
+    for local, additions, action, state_local, state_additions, re in zip(next_statesl, next_statesa, actions, statesl,
+                                                                          statesa, rewards):
+        computed_reward.append(reward(local, additions, action, state_local, state_additions, re))
+    # 这是得到的reward
+
+    return computed_reward
+
+
+def featurize2D(states):
+    feature2D = []
+    # 共9个矩阵
+    for element_board in rebuild_board(states["board"]):
+        feature2D.append(element_board)
+
+    feature2D.append(states["bomb_blast_strength"].tolist())
+    feature2D.append(states["bomb_life"].tolist())
+    feature2D.append(states["bomb_moving_direction"].tolist())
+    feature2D.append(states["flame_life"].tolist())
+
+    return feature2D
+
+
+def rebuild_board(board):
+    # 将board中数据分离，2D化
+    rigid = []
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 1:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        rigid.append(new_row)
+
+    wood = []
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 2:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        wood.append(new_row)
+
+    bomb = []
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 3:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        bomb.append(new_row)
+
+    flame = []
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 4:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        flame.append(new_row)
+
+    # fog部分
+    fog = []
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 5:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        fog.append(new_row)
+
+    # 充数的fog
+    # fog = [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+    #        [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+    #        [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+    #        [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+    #        [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+    #        [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+    #        [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+    #        [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]
+
+    power_up = []
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 6 or num == 7 or num == 8:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        power_up.append(new_row)
+
+    agent1 = []
+    # 如果是9为此处为agent,则取1.0
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 9:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        agent1.append(new_row)
+
+    agent2 = []
+    # 如果是10为此处为agent,则取1.0
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 10:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        agent2.append(new_row)
+
+    agent3 = []
+    # 如果是11为此处为agent,则取1.0
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 11:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        agent3.append(new_row)
+
+    agent4 = []
+    # 如果是12为此处为agent,则取1.0
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 12:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        agent4.append(new_row)
+
+    return rigid, wood, bomb, flame, fog, power_up, agent1, agent2, agent3, agent4
+
+
+def reward(state_local, state_additions, action, new_state_local, new_state_additions, rewards):
+    # set up reward
+    r_wood = 0.1
+    r_powerup = 0.3
+    r_put_bomb = 0.08
+    r_win = 1
+    r_fail = -5
+    r_kick = 0.3
+    r_kill_enemy_maybe = 0.5
+    r_dies = -3
+
+    rigid = np.array(state_local[0])
+    wood = np.array(state_local[1])
+    bomb = np.array(state_local[2])
+    agents = np.array(state_local[5])
+    # power_up = featurel[3]
+    position0 = int(state_additions[0])
+    position1 = int(state_additions[1])
+    p0 = int(new_state_additions[0])
+    p1 = int(new_state_additions[1])
+    ammo = int(state_additions[2])
+    blast_strength = int(state_additions[3])
+    can_kick = int(state_additions[4])
+    teammate = int(state_additions[5])
+    enemie1 = int(state_additions[6])
+    enemie2 = int(state_additions[7])
+    rewards = np.array(rewards)
+    reward = 0
+    # sagents = sl[4]
+    sbomb = np.array(new_state_local[2])
+
+    # reward_done
+    # print(rewards)
+    if rewards == 1:
+        reward += r_win
+    if rewards == -1:
+        reward += r_fail
+
+    # reward_powerup
+    sammo = int(new_state_additions[2])
+    if ammo > 1 and ammo > sammo:
+        reward += r_powerup
+    sstrength = int(new_state_additions[3])
+    if blast_strength > sstrength:
+        reward += r_powerup
+    skick = int(new_state_additions[4])
+    if can_kick and not skick:
+        reward += r_powerup
+    # print(action)
+
+    # reward_wood
+    if action == 5:
+        reward += r_put_bomb
+        bomb_flame = build_flame(position0, position1, rigid, blast_strength)
+        num_wood = np.count_nonzero(wood * bomb_flame == 1)
+        reward += num_wood * r_wood
+        '''
+        # test
+        print('rigid')
+        print(rigid)
+        print('position_bomb')
+        print(position_bomb)
+        print('f')
+        print(f)
+        print('l')
+        print(l)
+        print('bomb_flame')
+        print(bomb_flame)
+        print('num_wood')
+        print(num_wood)
+        print('-------------------------------------')
+        '''
+    """
+    exist_bomb = []
+    for row, rowbomb in enumerate(bomb):
+        for col, _ in enumerate(rowbomb):
+            if bomb[row, col] == 1:
+                exist_bomb.append((row, col))
+    #print(bomb)
+    #print(exist_bomb)
+
+    if exist_bomb:
+        for ebomb in exist_bomb:
+            bomb_flame1 = self.build_flame(ebomb[0], ebomb[1], rigid, blast_strength)
+            if bomb_flame1[position0, position1] == 1:
+                reward -= 0.5
+            #print(bomb_flame1)
+    """
+    # reward_kick
+    if sbomb[position0, position1] == 1 and rewards != -1:
+        reward += r_kick
+    '''
+    # reward_kill_enemy
+    enemy_position = []              #需要知道敌人位置
+    if int(action.item()) == 5:
+        bomb_position = np.array([position0,position1])
+        bomb_flame = self.build_flame(position0, position1, rigid, blast_strength)
+    if bomb_position in np.argwhere(bomb==1) and np.argwhere(enemy_position*bomb_flame == 1).size != 0:
+            reward += r_kill_enemy_maybe
+    '''
+
+    '''
+    # reward_dies
+    if is_alive == 0:
+        reward += r_dies
+    '''
+
+    return reward
+
+
+def build_flame(position0, position1, rigid, blast_strength):
+    position_bomb = np.array([position0, position1])
+    m = position_bomb[0]
+    n = position_bomb[1]
+    l = blast_strength - 1
+    f = [l, l, l, l]  # Scope of flame: up down left right
+    bomb_flame = np.zeros_like(rigid)
+
+    # 判断实体墙或边界是否阻断火焰
+    flame_up = np.zeros_like(bomb_flame)
+    flame_down = np.zeros_like(bomb_flame)
+    flame_left = np.zeros_like(bomb_flame)
+    flame_right = np.zeros_like(bomb_flame)
+    if m - f[0] < 0:  # 上边界
+        f[0] = m
+    flame_up[m - f[0]:m, n] = 1
+    if m + f[1] > bomb_flame.shape[0] - 1:  # 下边界
+        f[1] = bomb_flame.shape[0] - 1 - m
+    flame_down[m + 1:m + f[1] + 1, n] = 1
+    if n - f[2] < 0:  # 左边界
+        f[2] = n
+    flame_left[m, n - f[2]:n] = 1
+    if n + f[3] > bomb_flame.shape[0] - 1:  # 右边界
+        f[3] = bomb_flame.shape[0] - 1 - n
+    flame_right[m, n + 1:n + f[3] + 1] = 1
+
+    rigid_0 = flame_up * rigid
+    rigid_1 = flame_down * rigid
+    rigid_2 = flame_left * rigid
+    rigid_3 = flame_right * rigid
+    if np.argwhere(rigid_0 == 1).size != 0:  # 上实体墙
+        rigid_up = np.max(np.argwhere(rigid_0 == 1)[:, 0][0])
+        if rigid_up >= m - f[0]:
+            f[0] = m - rigid_up - 1
+    if np.argwhere(rigid_1 == 1).size != 0:  # 下实体墙
+        rigid_down = np.min(np.argwhere(rigid_1 == 1)[:, 0][0])
+        if rigid_down <= m + f[1]:
+            f[1] = rigid_down - m - 1
+    if np.argwhere(rigid_2 == 1).size != 0:  # 左实体墙
+        rigid_left = np.max(np.argwhere(rigid_2 == 1)[0, :][1])
+        if rigid_left >= n - f[2]:
+            f[2] = n - rigid_left - 1
+    if np.argwhere(rigid_3 == 1).size != 0:  # 右实体墙
+        rigid_right = np.min(np.argwhere(rigid_3 == 1)[0, :][1])
+        if rigid_right <= n + f[3]:
+            f[3] = rigid_right - n - 1
+    bomb_flame[m - f[0]:m + f[1] + 1, n] = 1
+    bomb_flame[m, n - f[2]:n + f[3] + 1] = 1
+
+    '''
+    # test
+    print('rigid')
+    print(rigid)
+    print('position_bomb')
+    print(position_bomb)
+    print('f')
+    print(f)
+    print('l')
+    print(l)
+    print('bomb_flame')
+    '''
+    # print(bomb_flame)
+    # print(blast_strength)
+    '''
+    print('num_wood')
+    print(num_wood)
+    print('-------------------------------------')
+    '''
+    return bomb_flame
diff --git a/DQN_new/DQNAgent.py b/DQN_new/DQNAgent.py
index 2921053..6da3d83 100644
--- a/DQN_new/DQNAgent.py
+++ b/DQN_new/DQNAgent.py
@@ -10,6 +10,8 @@
 from DQN_new import constants
 from replay_memory import replay_Memory
 import numpy as np
+import os
+import tensorflow as tf
 
 
 class DQNAgent(BaseAgent):
@@ -32,20 +34,20 @@ def __init__(self, character=characters.Bomber):
     def new_model(self):
 
         model = Sequential()
-        input_shape = (constants.MINIBATCH_SIZE, 9, 8, 8)
+        input_shape = (constants.MINIBATCH_SIZE, 12, 8, 8)
         model.add(Conv2D(256, 3, input_shape=input_shape[1:], activation="relu"))
         # print(model.output_shape)
-        model.add(MaxPooling2D(pool_size=(2, 2), data_format='channels_first'))
+        model.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))
         model.add(Dropout(0.2))
 
         model.add(Conv2D(256, 2, activation="relu"))
-        model.add(MaxPooling2D(pool_size=(2, 2), data_format='channels_first'))
+        model.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))
         model.add(Dropout(0.2))
 
         model.add(Flatten())
         model.add(Dense(64))
 
-        model.add(Dense(6, activation='linear'))
+        model.add(Dense(6, activation='softmax'))
         model.compile(loss="mse", optimizer=Adam(learning_rate=0.001), metrics=['accuracy'])
 
         return model
@@ -53,11 +55,28 @@ def new_model(self):
     def act(self, obs, action_space):
         return self.baseAgent.act(obs, Discrete(6))
 
-    def train(self, done):
+    def train(self, done, numOfEpisode):
 
         if self.buffer.size() < constants.MIN_REPLAY_MEMORY_SIZE:
             return
 
+        # if numOfEpisode == 0:
+        #     self.training_model.load_weights('./checkpoints/my_checkpoint')
+        #     self.trained_model.load_weights('./checkpoints/my_checkpoint')
+
+        if numOfEpisode % 999 == 0:
+            checkpoint_path = "/checkpoints/training1/cp.ckpt"
+            checkpoint_dir = os.path.dirname(checkpoint_path)
+
+            # 检查点重用
+            cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
+                                                             save_weights_only=True,
+                                                             verbose=1)
+        # 完成一次训练后存档参数
+        if numOfEpisode == 4999:
+            self.training_model.save_weights('/checkpoints/my_checkpoint')
+
+
         # 取样
         mini_batch = self.buffer.sample(constants.MINIBATCH_SIZE)
 
@@ -71,7 +90,7 @@ def train(self, done):
 
         # X为state，Y为所预测的action
         X = []
-        y = []
+        Y = []
 
         for index, (current_state, action, reward, new_current_state, done) in enumerate(mini_batch):
 
@@ -88,26 +107,211 @@ def train(self, done):
             current_qs[action] = next_q
 
             # 添加训练数据
-            X.append(current_state)
-            y.append(current_qs)
+            X.append(np.array(current_state))
+            # X.append(tf.reshape(current_state,(-1,12,8,8)))
+            Y.append(np.array(current_qs))
+
+            # 开始训练
+        # X = tf.reshape(X, (-1, 12, 8, 8))
+        # train_dataset = tf.data.Dataset.from_tensor_slices((X, Y))
+        # self.training_model.fit(train_dataset, verbose=0, shuffle=False)
 
-        # 开始训练
-        self.training_model.fit(np.array(X), np.array(y), batch_size=constants.MINIBATCH_SIZE, verbose=0, shuffle=False)
+        self.training_model.fit(np.array(X), np.array(Y), epochs=4, batch_size=constants.MINIBATCH_SIZE, verbose=0,
+                                shuffle=False)
 
         # 更新网络更新计数器
         if done:
             self.target_update_counter += 1
 
         # 网络更新计数器达到上限，更新网络
-        if self.target_update_counter > constants.UPDATE_TARGET_EVERY:
+        if self.target_update_counter > constants.UPDATE_EVERY:
             self.trained_model.set_weights(self.training_model.get_weights())
-            self.target_update_counter = 0
+        self.target_update_counter = 0
 
     def get_q_value(self, state):
-
-        return self.training_model.predict_on_batch(np.array(state).reshape(-1,9,8,8))
+        state_reshape = np.array(state).reshape(-1, 12, 8, 8)
+        return self.training_model.predict_on_batch(state_reshape)
 
         # epsilon衰减
 
+    def reward(self, featurel, featurea, action, sl, sa, rewards):
+        # set up reward
+        r_wood = 0.1
+        r_powerup = 0.3
+        r_put_bomb = 0.08
+        r_win = 1
+        r_fail = -5
+        r_kick = 0.3
+        r_kill_enemy_maybe = 0.5
+        r_dies = -3
+
+        rigid = featurel[0].numpy()
+        wood = featurel[1].numpy()
+        bomb = featurel[2].numpy()
+        agents = featurel[4].numpy()
+        power_up = featurel[3]
+        position0 = int(featurea[0].item())
+        position1 = int(featurea[1].item())
+        p0 = int(sa[0].item())
+        p1 = int(sa[1].item())
+        ammo = int(featurea[2].item())
+        blast_strength = int(featurea[3].item())
+        can_kick = int(featurea[4].item())
+        teammate = int(featurea[5].item())
+        enemies = int(featurea[6].item())
+        rewards = rewards.numpy()
+        reward = 0
+        # sagents = sl[4]
+        sbomb = sl[2].numpy()
+
+        # reward_done
+        # print(rewards)
+        if rewards == 1:
+            reward += r_win
+        if rewards == -1:
+            reward += r_fail
+
+        # reward_powerup
+        sammo = int(sa[2].item())
+        if ammo > 1 and ammo > sammo:
+            reward += r_powerup
+        sstrength = int(sa[3].item())
+        if blast_strength > sstrength:
+            reward += r_powerup
+        skick = int(sa[4].item())
+        if can_kick and not skick:
+            reward += r_powerup
+        # print(action)
+
+        # reward_wood
+        if int(action.item()) == 5:
+            reward += r_put_bomb
+            bomb_flame = self.build_flame(position0, position1, rigid, blast_strength)
+            num_wood = np.count_nonzero(wood * bomb_flame == 1)
+            reward += num_wood * r_wood
+            '''
+            # test
+            print('rigid')
+            print(rigid)
+            print('position_bomb')
+            print(position_bomb)
+            print('f')
+            print(f)
+            print('l')
+            print(l)
+            print('bomb_flame')
+            print(bomb_flame)
+            print('num_wood')
+            print(num_wood)
+            print('-------------------------------------')
+            '''
+        """
+        exist_bomb = []
+        for row, rowbomb in enumerate(bomb):
+            for col, _ in enumerate(rowbomb):
+                if bomb[row, col] == 1:
+                    exist_bomb.append((row, col))
+        #print(bomb)
+        #print(exist_bomb)
+
+        if exist_bomb:
+            for ebomb in exist_bomb:
+                bomb_flame1 = self.build_flame(ebomb[0], ebomb[1], rigid, blast_strength)
+                if bomb_flame1[position0, position1] == 1:
+                    reward -= 0.5
+                #print(bomb_flame1)
+        """
+        # reward_kick
+        if sbomb[position0, position1] == 1 and rewards != -1:
+            reward += r_kick
+        '''
+        # reward_kill_enemy
+        enemy_position = []              #需要知道敌人位置
+        if int(action.item()) == 5:
+            bomb_position = np.array([position0,position1])
+            bomb_flame = self.build_flame(position0, position1, rigid, blast_strength)
+        if bomb_position in np.argwhere(bomb==1) and np.argwhere(enemy_position*bomb_flame == 1).size != 0:
+                reward += r_kill_enemy_maybe
+        '''
+
+        '''
+        # reward_dies
+        if is_alive == 0:
+            reward += r_dies
+        '''
+
+        return reward
+
+    def build_flame(self, position0, position1, rigid, blast_strength):
+
+        position_bomb = np.array([position0, position1])
+        m = position_bomb[0]
+        n = position_bomb[1]
+        l = blast_strength - 1
+        f = [l, l, l, l]  # Scope of flame: up down left right
+        bomb_flame = np.zeros_like(rigid)
+
+        # 判断实体墙或边界是否阻断火焰
+        flame_up = np.zeros_like(bomb_flame)
+        flame_down = np.zeros_like(bomb_flame)
+        flame_left = np.zeros_like(bomb_flame)
+        flame_right = np.zeros_like(bomb_flame)
+        if m - f[0] < 0:  # 上边界
+            f[0] = m
+        flame_up[m - f[0]:m, n] = 1
+        if m + f[1] > bomb_flame.shape[0] - 1:  # 下边界
+            f[1] = bomb_flame.shape[0] - 1 - m
+        flame_down[m + 1:m + f[1] + 1, n] = 1
+        if n - f[2] < 0:  # 左边界
+            f[2] = n
+        flame_left[m, n - f[2]:n] = 1
+        if n + f[3] > bomb_flame.shape[0] - 1:  # 右边界
+            f[3] = bomb_flame.shape[0] - 1 - n
+        flame_right[m, n + 1:n + f[3] + 1] = 1
+
+        rigid_0 = flame_up * rigid
+        rigid_1 = flame_down * rigid
+        rigid_2 = flame_left * rigid
+        rigid_3 = flame_right * rigid
+        if np.argwhere(rigid_0 == 1).size != 0:  # 上实体墙
+            rigid_up = np.max(np.argwhere(rigid_0 == 1)[:, 0][0])
+            if rigid_up >= m - f[0]:
+                f[0] = m - rigid_up - 1
+        if np.argwhere(rigid_1 == 1).size != 0:  # 下实体墙
+            rigid_down = np.min(np.argwhere(rigid_1 == 1)[:, 0][0])
+            if rigid_down <= m + f[1]:
+                f[1] = rigid_down - m - 1
+        if np.argwhere(rigid_2 == 1).size != 0:  # 左实体墙
+            rigid_left = np.max(np.argwhere(rigid_2 == 1)[0, :][1])
+            if rigid_left >= n - f[2]:
+                f[2] = n - rigid_left - 1
+        if np.argwhere(rigid_3 == 1).size != 0:  # 右实体墙
+            rigid_right = np.min(np.argwhere(rigid_3 == 1)[0, :][1])
+            if rigid_right <= n + f[3]:
+                f[3] = rigid_right - n - 1
+        bomb_flame[m - f[0]:m + f[1] + 1, n] = 1
+        bomb_flame[m, n - f[2]:n + f[3] + 1] = 1
+
+        '''
+        # test
+        print('rigid')
+        print(rigid)
+        print('position_bomb')
+        print(position_bomb)
+        print('f')
+        print(f)
+        print('l')
+        print(l)
+        print('bomb_flame')
+        '''
+        # print(bomb_flame)
+        # print(blast_strength)
+        '''
+        print('num_wood')
+        print(num_wood)
+        print('-------------------------------------')
+        '''
+        return bomb_flame
+
     def epsilon_decay(self):
         self.epsilon = self.epsilon * self.eps_decay if self.epsilon > self.min_epsilon else self.epsilon
diff --git a/DQN_new/constants.py b/DQN_new/constants.py
index 8cc9d7f..b1edc9d 100644
--- a/DQN_new/constants.py
+++ b/DQN_new/constants.py
@@ -1,6 +1,6 @@
 MIN_REPLAY_MEMORY_SIZE = 1_000  # Minimum number of steps in a memory to start training
-MINIBATCH_SIZE = 256  # How many steps (samples) to use for training
-UPDATE_TARGET_EVERY = 10  # Terminal states (end of episodes)
+MINIBATCH_SIZE = 128  # How many steps (samples) to use for training
+UPDATE_EVERY = 10  # Terminal states (end of episodes)
 MAX_BUFFER_SIZE = 50_000
 DISCOUNT = 0.95
 
@@ -17,3 +17,6 @@
 AGGREGATE_STATS_EVERY = 300  # episodes
 SHOW_PREVIEW = False
 
+
+def MAX_BUFFER_SIZE_PRE():
+    return None
\ No newline at end of file
diff --git a/DQN_new/main.py b/DQN_new/main.py
index 1daa910..3a0b335 100644
--- a/DQN_new/main.py
+++ b/DQN_new/main.py
@@ -53,13 +53,13 @@ def main():
 
             # 每一定局数显示游戏画面
             # if constants.SHOW_PREVIEW and not episode % constants.SHOW_GAME:
-            #     env.render()
+            env.render()
 
             # 储存记忆
             agent1.buffer.append([state_feature, actions, reward, next_state_feature, done])
 
             # 学习!
-            agent1.train(done)
+            agent1.train(done, episode+1)
 
             # 更新state
             current_state = new_state
diff --git a/DQN_new/utility.py b/DQN_new/utility.py
index 3b14c0d..7792559 100644
--- a/DQN_new/utility.py
+++ b/DQN_new/utility.py
@@ -95,15 +95,49 @@ def rebuild_board(board):
                 new_row.append(0.0)
         power_up.append(new_row)
 
-    agents = []
-    # 如果是9,10,11,12代为此处为agent,则取1.0
+    agent1 = []
+    # 如果是9为此处为agent,则取1.0
     for row in board:
         new_row = []
         for num in row:
-            if num == 9 or num == 10 or num == 11 or num == 12:
+            if num == 9:
                 new_row.append(1.0)
             else:
                 new_row.append(0.0)
-        agents.append(new_row)
+        agent1.append(new_row)
+
+    agent2 = []
+    # 如果是10为此处为agent,则取1.0
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 10:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        agent2.append(new_row)
+
+    agent3 = []
+    # 如果是11为此处为agent,则取1.0
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 11:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        agent3.append(new_row)
+
+    agent4 = []
+    # 如果是12为此处为agent,则取1.0
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 12:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        agent4.append(new_row)
+
+    return rigid, wood, bomb, power_up, agent1, agent2, agent3, agent4
 
-    return rigid, wood,bomb, power_up, agents
diff --git a/DockerAgent/model/first_model.h5 b/DockerAgent/model/first_model.h5
new file mode 100644
index 0000000..1dfde1d
Binary files /dev/null and b/DockerAgent/model/first_model.h5 differ
diff --git a/Group_C/Agent1/keras_metadata.pb b/Group_C/Agent1/keras_metadata.pb
new file mode 100644
index 0000000..30e5627
--- /dev/null
+++ b/Group_C/Agent1/keras_metadata.pb
@@ -0,0 +1,12 @@
+
+�Broot"_tf_keras_sequential*�B{"name": "sequential", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 18, 11, 11]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_input"}}, {"class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 18, 11, 11]}, "dtype": "float32", "filters": 256, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_first", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": 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+�
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index 0000000..1d8e183
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diff --git a/Group_C/README.md b/Group_C/README.md
new file mode 100644
index 0000000..528dc3c
--- /dev/null
+++ b/Group_C/README.md
@@ -0,0 +1,71 @@
+# Architecture
+
+We were able to implement all the descendants of Rainbow DQN except Categorical DQN
+
+- [x] basic DQN
+- [x] Double DQN
+- [x] Prioritized Experience Replay
+- [x] Dueling Network Architectures
+- [x] Noisy Nets
+- [x] Multi Step Reinforcement Learning
+- [ ] Categorical DQN
+
+# Getting Started with our DQNAgent
+
+# Pre-requisites
+
+* [Python 3.6.0](https://www.python.org/downloads/release/python-360/)+ (including `pip`)
+* [Docker](https://www.docker.com/) (only needed for `DockerAgent`)
+* [tensorflow 2.6.2](https://www.tensorflow.org/hub/installation)
+* [Keras 2.6.0](https://keras.io/getting_started/)
+* Others are all included in [requirements](Group_C/requirements.txt)
+# Installation
+
+* Clone the repository
+```
+$ git clone https://github.com/oxFFFF-Q/Project_AI.git
+```
+
+## Pip
+
+* Install the `pommerman` package. This needs to be done every time the code is updated to get the
+latest modules
+```
+$ cd ~/playground
+$ pip install -U .
+```
+
+## Conda
+
+* Install the `pommerman` environment.
+```
+$ cd ~/playground
+$ conda env create -f env.yml
+$ conda activate pommerman
+```
+
+* To update the environment
+```
+$ conda env update -f env.yml --prune
+```
+
+# Launch the agent
+We have seperately trained models for player 1 [Agent1](Group_C/agents/Agent1.py) and player 3 [Agent3](Group_C/agents/Agent3.py). Run [main_test.py](Group_C/main_test.py) to test them palying against two [SimpleAgent](pommerman/agents/simple_agent.py).
+
+# Train your agent
+
+## A Simple Example
+
+Run [main_train.py](Group_C/main_train.py) to train our final DQN model for radio team competition of two [SimpleAgent](pommerman/agents/simple_agent.py) as enemies and a [SimpleAgent](pommerman/agents/simple_agent.py) as teammate.
+
+The training will not automatically stop, but need to be done manully, according to the given out report about the rewards. The paramaters will be recorded every 100 episodes. Run [main_save_model.py](Group_C/main_save_model.py) to save the model. The name of the model is required. The best one is usually among the last few models.
+
+## Use other strategies
+
+Select other names for `strategy` in [main_train.py](Group_C/main_train.py) to try other achietectures. Make sure of the consistency of the `strategy` in [main_save_model.py](Group_C/main_save_model.py).
+
+
+
+# Visualize the experiment results
+
+Our experiment results are all stored in [data](Group_C/result_image/data). Run [make_image.py](Group_C/result_image/make_image.py) to get a visualization of them.
diff --git a/Group_C/agents/Agent1.py b/Group_C/agents/Agent1.py
new file mode 100644
index 0000000..b753897
--- /dev/null
+++ b/Group_C/agents/Agent1.py
@@ -0,0 +1,115 @@
+from pommerman.agents import BaseAgent
+from Group_C.utility.action_filter import action_filter
+from Group_C.utility.communication import message
+import numpy as np
+import tensorflow as tf
+import collections
+import os
+
+
+class DQNAgent(BaseAgent):
+    """DQNAgent for running"""
+
+    def __init__(self, *args, **kwargs):
+        super(DQNAgent, self).__init__(*args, **kwargs)
+
+        self.gpu = os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+        self.DQN_model = tf.keras.models.load_model("./Agent1")
+        self.action_list = collections.deque([0, 0, 0], maxlen=3)
+
+    def action_predict(self, state):
+        state_feature = self.featurize2D(state)
+        state_reshape = tf.reshape(state_feature, (-1, 18, 11, 11))
+        q_table = self.DQN_model.predict(state_reshape)
+        return q_table
+
+    def act(self, observation, action_space):
+        mess, observation = message(observation)  # Communication: information sending, receiving processing
+        action = np.argmax(self.action_predict(observation)).tolist()
+        self.action_list.append(action)
+        action = action_filter(observation, self.action_list)
+        self.action_list[-1] = action
+        return [action, mess[0], mess[1]]
+
+    def episode_end(self, reward):
+        pass
+
+    def shutdown(self):
+        pass
+
+    def featurize2D(self, states, partially_obs=True):
+        # 18 channels in total
+        X = states["position"][0]
+        Y = states["position"][1]
+        shape = (11, 11)
+
+        # get limited observation
+        def get_partially_obs(states, X, Y):
+            # board = np.zeros(shape)
+            board = np.full(shape, 5)
+            for x in range(10):
+                for y in range(10):
+                    if X - 4 <= x <= X + 4 and Y - 4 <= y <= Y + 4:
+                        board[x][y] = states["board"][x][y]
+            states["board"] = board
+            return states
+
+        def get_matrix(board, key):
+            res = board[key]
+            return res.reshape(shape).astype(np.float64)
+
+        def get_map(board, item):
+            map = np.zeros(shape)
+            map[board == item] = 1
+            return map
+
+        if partially_obs:
+            states = get_partially_obs(states, X, Y)
+
+        board = get_matrix(states, "board")
+
+        path = get_map(board, 0)
+        rigid = get_map(board, 1)
+        wood = get_map(board, 2)
+        bomb = get_map(board, 3)
+        flame = get_map(board, 4)
+        fog = np.zeros(shape)
+        agent1 = get_map(board, 10)
+        agent2 = get_map(board, 11)
+        agent3 = get_map(board, 12)
+        agent4 = get_map(board, 13)
+
+        power_up = []
+        for row in board:
+            new_row = []
+            for num in row:
+                if num == 6 or num == 7 or num == 8:
+                    new_row.append(1)
+                else:
+                    new_row.append(0.0)
+            power_up.append(new_row)
+
+        bomb_blast_strength = get_matrix(states, 'bomb_blast_strength')
+        bomb_life = get_matrix(states, 'bomb_life')
+        bomb_moving_direction = get_matrix(states, 'bomb_moving_direction')
+        flame_life = get_matrix(states, 'flame_life')
+
+        ammo_2D, blast_strength_2D, can_kick_2D = self.rebuild_1D_element(states)
+
+        feature2D = [path, rigid, wood, bomb, flame, fog, power_up, agent1, agent2, agent3, agent4, bomb_blast_strength,
+                     bomb_life, bomb_moving_direction, flame_life, ammo_2D, blast_strength_2D, can_kick_2D]
+
+        return np.array(feature2D)
+
+    def rebuild_1D_element(self, states):
+        shape = (11, 11)
+        ammo = states["ammo"]
+        ammo_2D = np.full(shape, ammo)
+
+        blast_strength = states["blast_strength"]
+        blast_strength_2D = np.full(shape, blast_strength)
+
+        can_kick = states["can_kick"]
+        can_kick_2D = np.full(shape, int(can_kick))
+
+        return ammo_2D, blast_strength_2D, can_kick_2D
diff --git a/Group_C/agents/Agent3.py b/Group_C/agents/Agent3.py
new file mode 100644
index 0000000..0413272
--- /dev/null
+++ b/Group_C/agents/Agent3.py
@@ -0,0 +1,115 @@
+from pommerman.agents import BaseAgent
+from Group_C.utility.action_filter import action_filter
+from Group_C.utility.communication import message
+import numpy as np
+import tensorflow as tf
+import collections
+import os
+
+
+class DQNAgent(BaseAgent):
+    """DQNAgent for running"""
+
+    def __init__(self, *args, **kwargs):
+        super(DQNAgent, self).__init__(*args, **kwargs)
+
+        self.gpu = os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+        self.DQN_model = tf.keras.models.load_model("./Agent3")
+        self.action_list = collections.deque([0, 0, 0], maxlen=3)
+
+    def action_predict(self, state):
+        state_feature = self.featurize2D(state)
+        state_reshape = tf.reshape(state_feature, (-1, 18, 11, 11))
+        q_table = self.DQN_model.predict(state_reshape)
+        return q_table
+
+    def act(self, observation, action_space):
+        mess, observation = message(observation)  # Communication: information sending, receiving processing
+        action = np.argmax(self.action_predict(observation)).tolist()
+        self.action_list.append(action)
+        action = action_filter(observation, self.action_list)
+        self.action_list[-1] = action
+        return [action, mess[0], mess[1]]
+
+    def episode_end(self, reward):
+        pass
+
+    def shutdown(self):
+        pass
+
+    def featurize2D(self, states, partially_obs=True):
+        # 18 channels in total
+        X = states["position"][0]
+        Y = states["position"][1]
+        shape = (11, 11)
+
+        # get limited observation
+        def get_partially_obs(states, X, Y):
+            # board = np.zeros(shape)
+            board = np.full(shape, 5)
+            for x in range(10):
+                for y in range(10):
+                    if X - 4 <= x <= X + 4 and Y - 4 <= y <= Y + 4:
+                        board[x][y] = states["board"][x][y]
+            states["board"] = board
+            return states
+
+        def get_matrix(board, key):
+            res = board[key]
+            return res.reshape(shape).astype(np.float64)
+
+        def get_map(board, item):
+            map = np.zeros(shape)
+            map[board == item] = 1
+            return map
+
+        if partially_obs:
+            states = get_partially_obs(states, X, Y)
+
+        board = get_matrix(states, "board")
+
+        path = get_map(board, 0)
+        rigid = get_map(board, 1)
+        wood = get_map(board, 2)
+        bomb = get_map(board, 3)
+        flame = get_map(board, 4)
+        fog = np.zeros(shape)
+        agent1 = get_map(board, 10)
+        agent2 = get_map(board, 11)
+        agent3 = get_map(board, 12)
+        agent4 = get_map(board, 13)
+
+        power_up = []
+        for row in board:
+            new_row = []
+            for num in row:
+                if num == 6 or num == 7 or num == 8:
+                    new_row.append(1)
+                else:
+                    new_row.append(0.0)
+            power_up.append(new_row)
+
+        bomb_blast_strength = get_matrix(states, 'bomb_blast_strength')
+        bomb_life = get_matrix(states, 'bomb_life')
+        bomb_moving_direction = get_matrix(states, 'bomb_moving_direction')
+        flame_life = get_matrix(states, 'flame_life')
+
+        ammo_2D, blast_strength_2D, can_kick_2D = self.rebuild_1D_element(states)
+
+        feature2D = [path, rigid, wood, bomb, flame, fog, power_up, agent1, agent2, agent3, agent4, bomb_blast_strength,
+                     bomb_life, bomb_moving_direction, flame_life, ammo_2D, blast_strength_2D, can_kick_2D]
+
+        return np.array(feature2D)
+
+    def rebuild_1D_element(self, states):
+        shape = (11, 11)
+        ammo = states["ammo"]
+        ammo_2D = np.full(shape, ammo)
+
+        blast_strength = states["blast_strength"]
+        blast_strength_2D = np.full(shape, blast_strength)
+
+        can_kick = states["can_kick"]
+        can_kick_2D = np.full(shape, int(can_kick))
+
+        return ammo_2D, blast_strength_2D, can_kick_2D
diff --git a/Group_C/agents/DQNAgent_basic.py b/Group_C/agents/DQNAgent_basic.py
new file mode 100644
index 0000000..e7855ed
--- /dev/null
+++ b/Group_C/agents/DQNAgent_basic.py
@@ -0,0 +1,203 @@
+from keras.layers import Dense, Flatten, Conv2D
+from keras import Sequential
+from tensorflow.keras.optimizers import Adam
+from pommerman.agents import BaseAgent
+from pommerman.agents import RandomAgent
+from pommerman import characters
+from gym.spaces import Discrete
+from Group_C.utility.replay_memory import replay_Memory
+from Group_C.utility import constants
+import numpy as np
+import tensorflow as tf
+import copy
+
+
+class DQNAgent(BaseAgent):
+    """Basic DQN """
+
+    def __init__(self, character=characters.Bomber):
+        super(DQNAgent, self).__init__(character)
+        self.baseAgent = RandomAgent()
+
+        self.training_model = self.new_model()
+        self.trained_model = self.new_model()
+        self.trained_model.set_weights(self.training_model.get_weights())
+
+        self.epsilon = constants.epsilon
+        self.min_epsilon = constants.MIN_EPSILON
+        self.eps_decay = constants.EPSILON_DECAY
+        self.buffer = replay_Memory(constants.MAX_BUFFER_SIZE)
+        self.update_counter = 0
+
+    def new_model(self):
+
+        model = Sequential()
+        input_shape = (constants.MINIBATCH_SIZE, 18, 11, 11,)
+        model.add(Conv2D(256, 3, (1, 1), input_shape=input_shape[1:], activation="relu", data_format="channels_first",
+                         padding="same"))
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+        model.add(Flatten())
+        model.add(Dense(128, activation="relu"))
+        model.add(Dense(6, activation='linear'))
+        model.compile(loss="mse", optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])
+        model.summary()
+        return model
+
+    def action_choose(self, state):
+
+        state_reshape = tf.reshape(state, (-1, 18, 11, 11))
+        q_table = self.training_model.predict(state_reshape)
+        if np.random.random() <= 0.001:
+            print(q_table)
+        return q_table
+
+    def act(self, obs, action_space):
+        return self.baseAgent.act(obs, Discrete(6))
+
+    def save_buffer(self, state_feature, action, reward, next_state_feature, done, Data_processing=False):
+        """if you want rotate observation into other 3 corner, set Data_processing to True"""
+        if Data_processing:
+            self.data_processing(state_feature, action, reward, next_state_feature, done)
+        else:
+            self.buffer.append([state_feature, action, reward, next_state_feature, done])
+
+    def train(self):
+
+        if self.buffer.size() < constants.MIN_REPLAY_MEMORY_SIZE:
+            return
+
+        current_states, action, reward, new_states, done = self.buffer.sample_element(constants.MINIBATCH_SIZE)
+
+        # Take the current_states in the sample, get the Q value from the model
+        current_states_q = self.training_model.predict(current_states)
+
+        # Take next_state in the sample, get the Q value from the old network
+        new_states_q = self.trained_model.predict(new_states)
+
+        # X is the state, Y is the predicted action
+        states = []
+        actions = []
+
+        for index in range(constants.MINIBATCH_SIZE):
+
+            if done[index] is not True:
+                # Update Q value,
+                new_state_q = reward[index] + constants.DISCOUNT * np.max(new_states_q[index])
+            else:
+                new_state_q = reward[index]
+
+            # Update the Q value for the given states
+            current_better_q = current_states_q[index]
+            current_better_q[action[index]] = new_state_q
+
+            # Add training data
+            states.append(current_states[index])
+            actions.append(current_better_q)
+
+        # Start training
+        self.training_model.fit(np.array(states), np.array(actions), batch_size=constants.MINIBATCH_SIZE, verbose=0,
+                                shuffle=False)
+
+        # Update network update counters
+        if done:
+            self.update_counter += 1
+
+        # Network update counter reaches upper limit, update network
+        if self.update_counter > constants.UPDATE_EVERY:
+            self.trained_model.set_weights(self.training_model.get_weights())
+            self.update_counter = 0
+
+    # epsilon decay
+    def epsilon_decay(self):
+        self.epsilon = self.epsilon * self.eps_decay if self.epsilon > self.min_epsilon else self.epsilon
+
+    def save_weights(self, numOfEpisode):
+
+        # Archive parameters after training
+        # save weight every "save_weight" episode, change it in constants.py
+        if numOfEpisode % constants.save_weight == 0:
+            self.training_model.save_weights(('./checkpoints/episode{:}/episode{:}'.format(numOfEpisode, numOfEpisode)))
+            print("weights saved!")
+
+    def load_weights(self, weight_name):
+        self.training_model.load_weights('./checkpoints/{:}/{:}'.format(weight_name, weight_name))
+        self.trained_model.load_weights('./checkpoints/{:}/{:}'.format(weight_name, weight_name))
+        print("weights loaded!")
+
+    def save_model(self, model_name):
+        self.training_model.save("./{:}".format(model_name))
+
+    def data_processing(self, state_feature, action, reward, next_state_feature, done):
+        # Convert the top left map to another location
+        def convert_left_bottom(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=1)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=1)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 3
+            elif action == 2:
+                action = 4
+            elif action == 3:
+                action = 2
+            elif action == 4:
+                action = 1
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+        def convert_right_bottom(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=2)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=2)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 2
+            elif action == 2:
+                action = 1
+            elif action == 3:
+                action = 4
+            elif action == 4:
+                action = 3
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+        def convert_right_top(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=3)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=3)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 4
+            elif action == 2:
+                action = 3
+            elif action == 3:
+                action = 1
+            elif action == 4:
+                action = 2
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+            # Rotate
+
+        state_left_bottom, next_state_left_bottom, action_left_bottom = convert_left_bottom(state_feature,
+                                                                                            next_state_feature,
+                                                                                            action)
+        state_right_bottom, next_state_right_bottom, action_right_bottom = convert_right_bottom(state_feature,
+                                                                                                next_state_feature,
+                                                                                                action)
+        state_right_top, next_state_right_top, action_right_top = convert_right_top(state_feature, next_state_feature,
+                                                                                    action)
+        self.buffer.append([state_feature, action, reward, next_state_feature, done])
+        self.buffer.append([state_left_bottom, action_left_bottom, reward, next_state_left_bottom, done])
+        self.buffer.append([state_right_bottom, action_right_bottom, reward, next_state_right_bottom, done])
+        self.buffer.append([state_right_top, action_right_top, reward, next_state_right_top, done])
diff --git a/Group_C/agents/DQNAgent_double_dqn.py b/Group_C/agents/DQNAgent_double_dqn.py
new file mode 100644
index 0000000..518dd6e
--- /dev/null
+++ b/Group_C/agents/DQNAgent_double_dqn.py
@@ -0,0 +1,215 @@
+from keras.layers import Dense, Flatten, Conv2D
+from keras import Sequential
+from tensorflow.keras.optimizers import Adam
+from pommerman.agents import BaseAgent
+from pommerman.agents import RandomAgent
+from pommerman import characters
+from gym.spaces import Discrete
+from Group_C.utility.replay_memory import replay_Memory
+from Group_C.utility import constants
+import numpy as np
+import tensorflow as tf
+
+
+
+class DQNAgent(BaseAgent):
+    """DQN second try with keras"""
+
+    def __init__(self, character=characters.Bomber):
+        super(DQNAgent, self).__init__(character)
+        self.baseAgent = RandomAgent()
+
+        self.training_model = self.new_model()
+        self.trained_model = self.new_model()
+        self.trained_model.set_weights(self.training_model.get_weights())
+        # self.load_weights()
+
+        self.epsilon = constants.epsilon
+        self.min_epsilon = constants.MIN_EPSILON
+        self.eps_decay = constants.EPSILON_DECAY
+        self.buffer = replay_Memory(constants.MAX_BUFFER_SIZE)
+        self.update_counter = 0
+
+    def new_model(self):
+
+        model = Sequential()
+        input_shape = (constants.MINIBATCH_SIZE, 18, 11, 11,)
+        model.add(Conv2D(256, 3, (1, 1), input_shape=input_shape[1:], activation="relu", data_format="channels_first",
+                         padding="same"))
+        # print(model.output_shape)
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+        # print(model.output_shape)
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+        # print(model.output_shape)
+
+        model.add(Flatten())
+        model.add(Dense(128, activation="relu"))
+        model.add(Dense(6, activation='linear'))
+        model.compile(loss="mse", optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])
+        return model
+
+    def act(self, obs, action_space):
+        return self.baseAgent.act(obs, Discrete(6))
+
+    def train(self):
+
+        if self.buffer.size() < constants.MIN_REPLAY_MEMORY_SIZE:
+            return
+
+        current_states, action, reward, new_states, done = self.buffer.sample_element(constants.MINIBATCH_SIZE)
+
+        # Take the current_states in the sample, get the Q value from the model
+        current_states_q = self.training_model.predict(current_states)
+        double_new_states_q = self.training_model.predict(new_states)
+        # Take next_state in the sample, get the Q value from the old network
+        new_states_q = self.trained_model.predict(new_states)
+
+        # X is the state, Y is the predicted action
+        states = []
+        actions = []
+
+        for index in range(constants.MINIBATCH_SIZE):
+
+            if done[index] is not True:
+                # Update Q value, Double DQN
+                target = reward[index] + constants.DISCOUNT * new_states_q[index][np.argmax(double_new_states_q[index])]
+            else:
+                target = reward[index]
+
+            # estimate q-values based on current state
+            q_values = current_states_q[index]
+
+            # Update the Q value for the given states
+            current_better_q = np.array(q_values)
+            current_better_q[action[index]] = target
+
+            # Add training data
+            states.append(current_states[index])
+            actions.append(current_better_q)
+
+        # Use dedicated data api, but slower.
+        # states = tf.reshape(states, (-1, 12, 8, 8))
+        # train_dataset = tf.data.Dataset.from_tensor_slices((states, actions))
+        # self.training_model.fit(train_dataset, verbose=0, shuffle=False)
+
+        # Start training
+        self.training_model.fit(np.array(states), np.array(actions), batch_size=constants.MINIBATCH_SIZE, verbose=0,
+                                shuffle=False)
+
+        # Update network update counters
+        if done:
+            self.update_counter += 1
+
+        # Network update counter reaches upper limit, update network
+        if self.update_counter > constants.UPDATE_EVERY:
+            self.trained_model.set_weights(self.training_model.get_weights())
+            self.update_counter = 0
+
+    def save_buffer(self, state_feature, action, reward, next_state_feature, done, Data_processing=False):
+        """if you want rotate observation into other 3 corner, set Data_processing to True"""
+        if Data_processing:
+            self.data_processing(state_feature, action, reward, next_state_feature, done)
+        else:
+            self.buffer.append([state_feature, action, reward, next_state_feature, done])
+
+    def action_choose(self, state):
+
+        state_reshape = tf.reshape(state, (-1, 18, 11, 11))
+        q_table = self.training_model.predict(state_reshape)
+        if np.random.random() <= 0.001:
+            print(q_table)
+        return q_table
+
+    # epsilon decay
+    def epsilon_decay(self):
+        self.epsilon = self.epsilon * self.eps_decay if self.epsilon > self.min_epsilon else self.epsilon
+
+    def save_weights(self, numOfEpisode):
+
+        # Archive parameters after training
+        # save weight every "save_weight" episode, change it in constants.py
+        if numOfEpisode % constants.save_weight == 0:
+            self.training_model.save_weights(('./checkpoints/episode{:}/episode{:}'.format(numOfEpisode, numOfEpisode)))
+            print("weights saved!")
+
+    def load_weights(self, weight_name):
+        self.training_model.load_weights('./checkpoints/{:}/{:}'.format(weight_name, weight_name))
+        self.trained_model.load_weights('./checkpoints/{:}/{:}'.format(weight_name, weight_name))
+        print("weights loaded!")
+
+    def save_model(self, model_name):
+        self.training_model.save("./{:}".format(model_name))
+
+    def data_processing(self, state_feature, action, reward, next_state_feature, done):
+        # Convert the top left map to another location
+        def convert_left_bottom(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=1)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=1)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 3
+            elif action == 2:
+                action = 4
+            elif action == 3:
+                action = 2
+            elif action == 4:
+                action = 1
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+        def convert_right_bottom(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=2)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=2)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 2
+            elif action == 2:
+                action = 1
+            elif action == 3:
+                action = 4
+            elif action == 4:
+                action = 3
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+        def convert_right_top(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=3)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=3)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 4
+            elif action == 2:
+                action = 3
+            elif action == 3:
+                action = 1
+            elif action == 4:
+                action = 2
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+            # Rotate
+
+        state_left_bottom, next_state_left_bottom, action_left_bottom = convert_left_bottom(state_feature,
+                                                                                            next_state_feature,
+                                                                                            action)
+        state_right_bottom, next_state_right_bottom, action_right_bottom = convert_right_bottom(state_feature,
+                                                                                                next_state_feature,
+                                                                                                action)
+        state_right_top, next_state_right_top, action_right_top = convert_right_top(state_feature, next_state_feature,
+                                                                                    action)
+        self.buffer.append([state_feature, action, reward, next_state_feature, done])
+        self.buffer.append([state_left_bottom, action_left_bottom, reward, next_state_left_bottom, done])
+        self.buffer.append([state_right_bottom, action_right_bottom, reward, next_state_right_bottom, done])
+        self.buffer.append([state_right_top, action_right_top, reward, next_state_right_top, done])
\ No newline at end of file
diff --git a/Group_C/agents/DQNAgent_dueling_dqn.py b/Group_C/agents/DQNAgent_dueling_dqn.py
new file mode 100644
index 0000000..d40f766
--- /dev/null
+++ b/Group_C/agents/DQNAgent_dueling_dqn.py
@@ -0,0 +1,236 @@
+from tensorflow.keras.optimizers import Adam
+from pommerman.agents import BaseAgent
+from pommerman.agents import RandomAgent
+from pommerman import characters
+from gym.spaces import Discrete
+from Group_C.utility.replay_memory import replay_Memory
+from Group_C.utility import constants
+import numpy as np
+import tensorflow as tf
+import tensorflow.keras as keras
+
+class Dueling_Model(tf.keras.Model):
+
+    def __init__(self, ):
+        super(Dueling_Model, self).__init__()
+
+        self.c1 = keras.layers.Conv2D(256, 3, (1, 1), input_shape=(constants.MINIBATCH_SIZE, 18, 11, 11,)[1:],
+                                      activation="relu", data_format="channels_first",
+                                      padding="same")
+        self.c2 = keras.layers.Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same")
+
+        self.c3 = keras.layers.Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same")
+        self.flatten = keras.layers.Flatten()
+        self.l1 = keras.layers.Dense(128, activation="relu")
+
+        self.V = keras.layers.Dense(1, activation=None)
+        self.A = keras.layers.Dense(6, activation=None)
+
+    def call(self, inputs):
+        x = self.c1(inputs)
+        x = self.c2(x)
+        x = self.c3(x)
+        x = self.flatten(x)
+        x = self.l1(x)
+
+        V = self.V(x)
+        A = self.A(x)
+        mean = tf.math.reduce_mean(A, axis=1, keepdims=True)
+
+        output = V + tf.subtract(A, mean)
+        return output
+
+    def advantage(self, state):
+        x = self.c1(state)
+        x = self.c2(x)
+        x = self.c3(x)
+        x = self.flatten(x)
+        x = self.l1(x)
+        A = self.A(x)
+        return A
+
+
+class DQNAgent(BaseAgent):
+    """DQN second try with keras"""
+
+    def __init__(self, character=characters.Bomber):
+        super(DQNAgent, self).__init__(character)
+        self.baseAgent = RandomAgent()
+
+        self.training_model = Dueling_Model()
+        self.trained_model = Dueling_Model()
+
+        self.trained_model.set_weights(self.training_model.get_weights())
+        # self.load_weights()
+
+        self.epsilon = constants.epsilon
+        self.min_epsilon = constants.MIN_EPSILON
+        self.eps_decay = constants.EPSILON_DECAY
+        self.buffer = replay_Memory(constants.MAX_BUFFER_SIZE)
+        self.update_counter = 0
+
+        self.training_model.compile(loss="mse", optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])
+        self.trained_model.compile(loss="mse", optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])
+
+    def act(self, obs, action_space):
+        return self.baseAgent.act(obs, Discrete(6))
+
+    def train(self):
+
+        if self.buffer.size() < constants.MIN_REPLAY_MEMORY_SIZE:
+            return
+
+        current_states, action, reward, new_states, done = self.buffer.sample_element(constants.MINIBATCH_SIZE)
+
+        # Take the current_states in the sample, get the Q value from the model
+        current_states_q = self.training_model.call(current_states)
+        # double_new_qs = self.training_model.call(new_states)
+
+        # Take next_state in the sample, get the Q value from the old network
+        # new_states_q = tf.math.reduce_max(self.trained_model.call(new_states), axis=1, keepdims=True)
+        new_states_q = self.trained_model.call(new_states)
+
+        # X is the state, Y is the predicted action
+        states = []
+        actions = []
+
+        for index in range(constants.MINIBATCH_SIZE):
+
+            if done[index] is not True:
+                # Update Q value
+                new_state_q = reward[index] + constants.DISCOUNT * np.max(new_states_q[index])
+            else:
+                new_state_q = reward[index]
+
+            # estimate q-values based on current state
+            q_values = current_states_q[index]
+            # Update the Q value for the given states
+            current_better_q = np.array(q_values)
+            current_better_q[action[index]] = new_state_q
+
+            # Add training data
+            states.append(current_states[index])
+            actions.append(current_better_q)
+
+        # Start training
+        self.training_model.fit(np.array(states), np.array(actions), batch_size=constants.MINIBATCH_SIZE, verbose=0,
+                                shuffle=False)
+        # Update network update counters
+        if done:
+            self.update_counter += 1
+
+        # Network update counter reaches upper limit, update network
+        if self.update_counter > constants.UPDATE_EVERY:
+            self.trained_model.set_weights(self.training_model.get_weights())
+            self.update_counter = 0
+
+    def save_buffer(self, state_feature, action, reward, next_state_feature, done, Data_processing=False):
+        """if you want rotate observation into other 3 corner, set Data_processing to True"""
+        if Data_processing:
+            self.data_processing(state_feature, action, reward, next_state_feature, done)
+        else:
+            self.buffer.append([state_feature, action, reward, next_state_feature, done])
+
+    def action_choose(self, state):
+        state_reshape = tf.reshape(state, (-1, 18, 11, 11))
+        q_table = self.training_model.advantage(state_reshape)
+        if np.random.random() <= 0.001:
+            print(q_table)
+        return q_table
+
+    # epsilon decay
+    def epsilon_decay(self):
+        self.epsilon = self.epsilon * self.eps_decay if self.epsilon > self.min_epsilon else self.epsilon
+
+    def save_buffer(self, state_feature, action, reward, next_state_feature, done):
+        self.buffer.append([state_feature, action, reward, next_state_feature, done])
+
+    def save_weights(self, numOfEpisode):
+
+        # Archive parameters after training
+        # save weight every "save_weight" episode, change it in constants.py
+        if numOfEpisode % constants.save_weight == 0:
+            self.training_model.save_weights(('./checkpoints/episode{:}/episode{:}'.format(numOfEpisode, numOfEpisode)))
+            print("weights saved!")
+
+    def load_weights(self, weight_name):
+        self.training_model.load_weights('./checkpoints/{:}/{:}'.format(weight_name, weight_name))
+        self.trained_model.load_weights('./checkpoints/{:}/{:}'.format(weight_name, weight_name))
+        print("weights loaded!")
+
+    def save_model(self, model_name):
+        self.training_model.save("./{:}".format(model_name))
+
+    def data_processing(self, state_feature, action, reward, next_state_feature, done):
+        # Convert the top left map to another location
+        def convert_left_bottom(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=1)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=1)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 3
+            elif action == 2:
+                action = 4
+            elif action == 3:
+                action = 2
+            elif action == 4:
+                action = 1
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+        def convert_right_bottom(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=2)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=2)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 2
+            elif action == 2:
+                action = 1
+            elif action == 3:
+                action = 4
+            elif action == 4:
+                action = 3
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+        def convert_right_top(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=3)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=3)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 4
+            elif action == 2:
+                action = 3
+            elif action == 3:
+                action = 1
+            elif action == 4:
+                action = 2
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+            # Rotate
+
+        state_left_bottom, next_state_left_bottom, action_left_bottom = convert_left_bottom(state_feature,
+                                                                                            next_state_feature,
+                                                                                            action)
+        state_right_bottom, next_state_right_bottom, action_right_bottom = convert_right_bottom(state_feature,
+                                                                                                next_state_feature,
+                                                                                                action)
+        state_right_top, next_state_right_top, action_right_top = convert_right_top(state_feature, next_state_feature,
+                                                                                    action)
+        self.buffer.append([state_feature, action, reward, next_state_feature, done])
+        self.buffer.append([state_left_bottom, action_left_bottom, reward, next_state_left_bottom, done])
+        self.buffer.append([state_right_bottom, action_right_bottom, reward, next_state_right_bottom, done])
+        self.buffer.append([state_right_top, action_right_top, reward, next_state_right_top, done])
diff --git a/Group_C/agents/DQNAgent_final.py b/Group_C/agents/DQNAgent_final.py
new file mode 100644
index 0000000..d0feefb
--- /dev/null
+++ b/Group_C/agents/DQNAgent_final.py
@@ -0,0 +1,144 @@
+from keras.layers import Dense, Flatten, Conv2D
+from keras import Sequential
+from tensorflow.keras.optimizers import Adam
+from pommerman.agents import BaseAgent
+from pommerman.agents import RandomAgent
+from pommerman import characters
+from gym.spaces import Discrete
+from Group_C.utility.replay_memory import replay_Memory
+from Group_C.utility import constants
+import numpy as np
+import tensorflow as tf
+
+
+class DQNAgent(BaseAgent):
+    """DQN combine double DQN and priority memory"""
+
+    def __init__(self, character=characters.Bomber):
+        super(DQNAgent, self).__init__(character)
+        self.baseAgent = RandomAgent()
+
+        self.training_model = self.new_model()
+        self.trained_model = self.new_model()
+        self.trained_model.set_weights(self.training_model.get_weights())
+
+        self.epsilon = constants.epsilon
+        self.min_epsilon = constants.MIN_EPSILON
+        self.eps_decay = constants.EPSILON_DECAY
+        self.buffer = replay_Memory(constants.MAX_BUFFER_SIZE)
+        self.update_counter = 0
+
+    def new_model(self):
+
+        model = Sequential()
+        input_shape = (constants.MINIBATCH_SIZE, 18, 11, 11,)
+        model.add(Conv2D(256, 3, (1, 1), input_shape=input_shape[1:], activation="relu", data_format="channels_first",
+                         padding="same"))
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+
+        model.add(Flatten())
+        model.add(Dense(128, activation="relu"))
+        model.add(Dense(6, activation='linear'))
+        model.compile(loss="mse", optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])
+        return model
+
+    def act(self, obs, action_space):
+        return self.baseAgent.act(obs, Discrete(6))
+
+    def train(self):
+
+        if self.buffer.size() < constants.MIN_REPLAY_MEMORY_SIZE:
+            return
+
+        current_states, action, reward, new_states, done = self.buffer.sample_element_pri(constants.MINIBATCH_SIZE)
+
+        # Take the current_states in the sample, get the Q value from the model
+        current_states_q = self.training_model.predict(current_states)
+        double_new_states_q = self.training_model.predict(new_states)
+        # Take next_state in the sample, get the Q value from the old network
+        new_states_q = self.trained_model.predict(new_states)
+
+        # X is the state, Y is the predicted action
+        states = []
+        actions = []
+
+        for index in range(constants.MINIBATCH_SIZE):
+
+            if done[index] is not True:
+                # Update Q value, Double DQN
+                target = reward[index] + constants.DISCOUNT * new_states_q[index][np.argmax(double_new_states_q[index])]
+            else:
+                target = reward[index]
+
+            # estimate q-values based on current state
+            q_values = current_states_q[index]
+
+            # Update the Q value for the given states
+            current_better_q = np.array(q_values)
+            current_better_q[action[index]] = target
+            # current_better_q = tf.convert_to_tensor(current_better_q)
+
+            # Add training data
+            states.append(current_states[index])
+            actions.append(current_better_q)
+
+        # Start training
+        self.training_model.fit(np.array(states), np.array(actions), batch_size=constants.MINIBATCH_SIZE, verbose=0,
+                                shuffle=False)
+
+        # Update network update counters
+        if done:
+            self.update_counter += 1
+
+        # Network update counter reaches upper limit, update network
+        if self.update_counter > constants.UPDATE_EVERY:
+            self.trained_model.set_weights(self.training_model.get_weights())
+            self.update_counter = 0
+
+    def calculate_td_error(self, state, action, reward, new_state, done):
+        state_ = tf.reshape(state, (-1, 18, 11, 11))
+        new_state_ = tf.reshape(new_state, (-1, 18, 11, 11))
+        q_values = self.training_model.predict(state_)[0]
+        q_value = q_values[action]
+        if not done:
+            target = reward + constants.DISCOUNT * \
+                     self.trained_model.predict(new_state_)[0, np.argmax(self.training_model.predict(new_state_))]
+        else:
+            target = reward
+
+        td_error = target - q_value
+        return td_error
+
+    def action_choose(self, state):
+
+        state_reshape = tf.reshape(state, (-1, 18, 11, 11))
+        q_table = self.training_model.predict(state_reshape)
+        if np.random.random() <= 0.001:
+            print(q_table)
+        return q_table
+
+    def save_buffer(self, state_feature, action, reward, next_state_feature, done):
+        td_error = self.calculate_td_error(state_feature, action, reward, next_state_feature, done)
+        self.buffer.append_pri(state_feature, action, reward, next_state_feature, done, td_error)
+
+        # epsilon decay
+
+    def epsilon_decay(self):
+        self.epsilon = self.epsilon * self.eps_decay if self.epsilon > self.min_epsilon else self.epsilon
+
+    def save_weights(self, numOfEpisode):
+
+        # Archive parameters after training
+        # save weight every "save_weight" episode, change it in constants.py
+        if numOfEpisode % constants.save_weight == 0:
+            self.training_model.save_weights(('./checkpoints/episode{:}/episode{:}'.format(numOfEpisode, numOfEpisode)))
+            print("weights saved!")
+
+    def load_weights(self, weight_name):
+        self.training_model.load_weights('./checkpoints/{:}/{:}'.format(weight_name, weight_name))
+        self.trained_model.load_weights('./checkpoints/{:}/{:}'.format(weight_name, weight_name))
+        print("weights loaded!")
+
+    def save_model(self, model_name):
+        self.training_model.save("./{:}".format(model_name))
diff --git a/Group_C/agents/DQNAgent_multi_steps.py b/Group_C/agents/DQNAgent_multi_steps.py
new file mode 100644
index 0000000..dd2050b
--- /dev/null
+++ b/Group_C/agents/DQNAgent_multi_steps.py
@@ -0,0 +1,215 @@
+from keras.layers import Dense, Flatten, Conv2D
+from keras import Sequential
+from tensorflow.keras.optimizers import Adam
+from pommerman.agents import BaseAgent
+from pommerman.agents import RandomAgent
+from pommerman import characters
+from gym.spaces import Discrete
+from Group_C.utility.replay_memory import replay_Memory
+from Group_C.utility import constants
+import numpy as np
+import tensorflow as tf
+import copy
+
+
+class DQNAgent(BaseAgent):
+    """DQN second try with keras"""
+
+    def __init__(self, character=characters.Bomber):
+        super(DQNAgent, self).__init__(character)
+        self.baseAgent = RandomAgent()
+
+        self.training_model = self.new_model()
+        self.trained_model = self.new_model()
+        self.trained_model.set_weights(self.training_model.get_weights())
+        # self.load_weights()
+
+        self.epsilon = constants.epsilon
+        self.min_epsilon = constants.MIN_EPSILON
+        self.eps_decay = constants.EPSILON_DECAY
+        self.buffer = replay_Memory(constants.MAX_BUFFER_SIZE)
+        self.update_counter = 0
+
+    def new_model(self):
+
+        model = Sequential()
+        input_shape = (constants.MINIBATCH_SIZE, 18, 11, 11,)
+        model.add(Conv2D(256, 3, (1, 1), input_shape=input_shape[1:], activation="relu", data_format="channels_first",
+                         padding="same"))
+        # print(model.output_shape)
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+        # print(model.output_shape)
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+        # print(model.output_shape)
+
+        model.add(Flatten())
+        model.add(Dense(128, activation="relu"))
+        model.add(Dense(6, activation='linear'))
+        model.compile(loss="mse", optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])
+        model.summary()
+        return model
+
+    def action_choose(self, state):
+
+        state_reshape = tf.reshape(state, (-1, 18, 11, 11))
+        q_table = self.training_model.predict(state_reshape)
+        if np.random.random() <= 0.001:
+            print(q_table)
+        return q_table
+
+    def act(self, obs, action_space):
+        return self.baseAgent.act(obs, Discrete(6))
+
+    def save_buffer(self, state_feature, action, reward, next_state_feature, done, Data_processing=False):
+        """if you want rotate observation into other 3 corner, set Data_processing to True"""
+        if Data_processing:
+            self.data_processing(state_feature, action, reward, next_state_feature, done)
+        else:
+            self.buffer.append([state_feature, action, reward, next_state_feature, done])
+
+    def train(self):
+        if self.buffer.size() < constants.MIN_REPLAY_MEMORY_SIZE:
+            return
+
+        current_states, action, reward, new_states, done = self.buffer.sample_element(constants.MINIBATCH_SIZE)
+
+        # Take the current_states in the sample, get the Q value from the model
+        current_states_q = self.training_model.predict(current_states)
+
+        # Take next_state in the sample, get the Q value from the old network
+        new_states_q = self.trained_model.predict(new_states)
+
+        # X is the state, Y is the predicted action
+        states = []
+        actions = []
+
+        for index in range(constants.MINIBATCH_SIZE):
+
+            if done[index] is not True:
+                # Update Q value
+                new_state_q = reward[index] + constants.DISCOUNT * np.max(new_states_q[index])
+            else:
+                new_state_q = reward[index]
+
+            # Update the Q value for the given states
+            current_better_q = current_states_q[index]
+            current_better_q[action[index]] = new_state_q
+
+            # Add training data
+            states.append(current_states[index])
+            actions.append(current_better_q)
+
+
+        # Use dedicated data api, but slower.
+        # states = tf.reshape(states, (-1, 12, 8, 8))
+        # train_dataset = tf.data.Dataset.from_tensor_slices((states, actions))
+        # self.training_model.fit(train_dataset, verbose=0, shuffle=False)
+
+        # Start training
+        self.training_model.fit(np.array(states), np.array(actions), batch_size=constants.MINIBATCH_SIZE, verbose=0,
+                                shuffle=False)
+
+        # Update network update counters
+        if done:
+            self.update_counter += 1
+
+        # Network update counter reaches upper limit, update network
+        if self.update_counter > constants.UPDATE_EVERY:
+            self.trained_model.set_weights(self.training_model.get_weights())
+            self.update_counter = 0
+
+        # epsilon decay
+
+    def epsilon_decay(self):
+        self.epsilon = self.epsilon * self.eps_decay if self.epsilon > self.min_epsilon else self.epsilon
+
+    def save_weights(self, numOfEpisode):
+
+        # Archive parameters after training
+        # save weight every "save_weight" episode, change it in constants.py
+        if numOfEpisode % constants.save_weight == 0:
+            self.training_model.save_weights(('./checkpoints/episode{:}/episode{:}'.format(numOfEpisode, numOfEpisode)))
+            print("weights saved!")
+
+    def load_weights(self, weight_name):
+        self.training_model.load_weights('./checkpoints/{:}/{:}'.format(weight_name, weight_name))
+        self.trained_model.load_weights('./checkpoints/{:}/{:}'.format(weight_name, weight_name))
+        print("weights loaded!")
+
+    def save_model(self, model_name):
+        self.training_model.save("./{:}".format(model_name))
+
+    def data_processing(self, state_feature, action, reward, next_state_feature, done):
+        # Convert the top left map to another location
+        def convert_left_bottom(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=1)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=1)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 3
+            elif action == 2:
+                action = 4
+            elif action == 3:
+                action = 2
+            elif action == 4:
+                action = 1
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+        def convert_right_bottom(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=2)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=2)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 2
+            elif action == 2:
+                action = 1
+            elif action == 3:
+                action = 4
+            elif action == 4:
+                action = 3
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+        def convert_right_top(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=3)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=3)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 4
+            elif action == 2:
+                action = 3
+            elif action == 3:
+                action = 1
+            elif action == 4:
+                action = 2
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+            # Rotate
+
+        state_left_bottom, next_state_left_bottom, action_left_bottom = convert_left_bottom(state_feature,
+                                                                                            next_state_feature,
+                                                                                            action)
+        state_right_bottom, next_state_right_bottom, action_right_bottom = convert_right_bottom(state_feature,
+                                                                                                next_state_feature,
+                                                                                                action)
+        state_right_top, next_state_right_top, action_right_top = convert_right_top(state_feature, next_state_feature,
+                                                                                    action)
+        self.buffer.append([state_feature, action, reward, next_state_feature, done])
+        self.buffer.append([state_left_bottom, action_left_bottom, reward, next_state_left_bottom, done])
+        self.buffer.append([state_right_bottom, action_right_bottom, reward, next_state_right_bottom, done])
+        self.buffer.append([state_right_top, action_right_top, reward, next_state_right_top, done])
+
diff --git a/Group_C/agents/DQNAgent_noisy.py b/Group_C/agents/DQNAgent_noisy.py
new file mode 100644
index 0000000..23eaf75
--- /dev/null
+++ b/Group_C/agents/DQNAgent_noisy.py
@@ -0,0 +1,206 @@
+import copy
+import numpy as np
+import tensorflow as tf
+import tensorflow_addons as tfa
+from keras.layers import Dense, Flatten, Conv2D
+from keras import Sequential
+from tensorflow.keras.optimizers import Adam
+from pommerman.agents import BaseAgent
+from pommerman.agents import RandomAgent
+from pommerman import characters
+from gym.spaces import Discrete
+from Group_C.utility.replay_memory import replay_Memory
+from Group_C.utility import constants
+
+
+class DQNAgent(BaseAgent):
+    """DQN second try with keras"""
+
+    def __init__(self, character=characters.Bomber):
+        super(DQNAgent, self).__init__(character)
+        self.baseAgent = RandomAgent()
+
+        self.training_model = self.new_model()
+        self.trained_model = self.new_model()
+        self.trained_model.set_weights(self.training_model.get_weights())
+        # self.load_weights()
+
+        self.epsilon = constants.epsilon
+        self.min_epsilon = constants.MIN_EPSILON
+        self.eps_decay = constants.EPSILON_DECAY
+        self.buffer = replay_Memory(constants.MAX_BUFFER_SIZE)
+        self.update_counter = 0
+
+    def new_model(self):
+
+        model = Sequential()
+        input_shape = (constants.MINIBATCH_SIZE, 18, 11, 11,)
+        model.add(Conv2D(256, 3, (1, 1), input_shape=input_shape[1:], activation="relu", data_format="channels_first",
+                         padding="same"))
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+
+        model.add(Flatten())
+        model.add(tfa.layers.NoisyDense(128, activation="relu"))
+        model.add(Dense(6, activation='linear'))
+        model.compile(loss="mse", optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])
+        return model
+
+    def act(self, obs, action_space):
+        return self.baseAgent.act(obs, Discrete(6))
+
+    def train(self):
+
+        if self.buffer.size() < constants.MIN_REPLAY_MEMORY_SIZE:
+            return
+
+        current_states, action, reward, new_states, done = self.buffer.sample_element(constants.MINIBATCH_SIZE)
+
+        # Take the current_states in the sample, get the Q value from the model
+        current_states_q = self.training_model.predict(current_states)
+
+        # Take next_state in the sample, get the Q value from the old network
+        new_states_q = self.trained_model.predict(new_states)
+
+        # X is the state, Y is the predicted action
+        states = []
+        actions = []
+
+        for index in range(constants.MINIBATCH_SIZE):
+
+            if done[index] is not True:
+                # Update Q value
+                new_state_q = reward[index] + constants.DISCOUNT * np.max(new_states_q[index])
+            else:
+                new_state_q = reward[index]
+
+            # Update the Q value for the given states
+            current_better_q = current_states_q[index]
+            current_better_q[action[index]] = new_state_q
+
+            # Add training data
+            states.append(current_states[index])
+            actions.append(current_better_q)
+
+        # Start training
+        self.training_model.fit(np.array(states), np.array(actions), batch_size=constants.MINIBATCH_SIZE, verbose=0,
+                                shuffle=False)
+
+        # Update network update counters
+        if done:
+            self.update_counter += 1
+
+        # Network update counter reaches upper limit, update network
+        if self.update_counter > constants.UPDATE_EVERY:
+            self.trained_model.set_weights(self.training_model.get_weights())
+            self.update_counter = 0
+
+    def action_choose(self, state):
+
+        state_reshape = tf.reshape(state, (-1, 18, 11, 11))
+        q_table = self.training_model.predict(state_reshape)
+        if np.random.random() <= 0.001:
+            print(q_table)
+        return q_table
+
+    def save_buffer(self, state_feature, action, reward, next_state_feature, done, Data_processing=False):
+        """if you want rotate observation into other 3 corner, set Data_processing to True"""
+        if Data_processing:
+            self.data_processing(state_feature, action, reward, next_state_feature, done)
+        else:
+            self.buffer.append([state_feature, action, reward, next_state_feature, done])
+
+        # epsilon decay
+    def epsilon_decay(self):
+        self.epsilon = self.epsilon * self.eps_decay if self.epsilon > self.min_epsilon else self.epsilon
+
+    def save_weights(self, numOfEpisode):
+
+        # Archive parameters after training
+        # save weight every "save_weight" episode, change it in constants.py
+        if numOfEpisode % constants.save_weight == 0:
+            self.training_model.save_weights(('./checkpoints/episode{:}/episode{:}'.format(numOfEpisode, numOfEpisode)))
+            print("weights saved!")
+
+    def load_weights(self, weight_name):
+        self.training_model.load_weights('./checkpoints/{:}/{:}'.format(weight_name, weight_name))
+        self.trained_model.load_weights('./checkpoints/{:}/{:}'.format(weight_name, weight_name))
+        print("weights loaded!")
+
+    def save_model(self, model_name):
+        self.training_model.save("./{:}".format(model_name))
+
+    def data_processing(self, state_feature, action, reward, next_state_feature, done):
+        # Convert the top left map to another location
+        def convert_left_bottom(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=1)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=1)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 3
+            elif action == 2:
+                action = 4
+            elif action == 3:
+                action = 2
+            elif action == 4:
+                action = 1
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+        def convert_right_bottom(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=2)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=2)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 2
+            elif action == 2:
+                action = 1
+            elif action == 3:
+                action = 4
+            elif action == 4:
+                action = 3
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+        def convert_right_top(state_feature, next_state_feature, action):
+            state_feature_left_bottom = []
+            for board in state_feature:
+                state = np.rot90(board, k=3)
+                state_feature_left_bottom.append(state)
+            next_state_feature_left_bottom = []
+            for board in next_state_feature:
+                state = np.rot90(board, k=3)
+                next_state_feature_left_bottom.append(state)
+            if action == 1:
+                action = 4
+            elif action == 2:
+                action = 3
+            elif action == 3:
+                action = 1
+            elif action == 4:
+                action = 2
+            return np.array(state_feature_left_bottom), np.array(next_state_feature_left_bottom), action
+
+            # Rotate
+
+        state_left_bottom, next_state_left_bottom, action_left_bottom = convert_left_bottom(state_feature,
+                                                                                            next_state_feature,
+                                                                                            action)
+        state_right_bottom, next_state_right_bottom, action_right_bottom = convert_right_bottom(state_feature,
+                                                                                                next_state_feature,
+                                                                                                action)
+        state_right_top, next_state_right_top, action_right_top = convert_right_top(state_feature, next_state_feature,
+                                                                                    action)
+        self.buffer.append([state_feature, action, reward, next_state_feature, done])
+        self.buffer.append([state_left_bottom, action_left_bottom, reward, next_state_left_bottom, done])
+        self.buffer.append([state_right_bottom, action_right_bottom, reward, next_state_right_bottom, done])
+        self.buffer.append([state_right_top, action_right_top, reward, next_state_right_top, done])
+
diff --git a/Group_C/agents/DQNAgent_priority_memory.py b/Group_C/agents/DQNAgent_priority_memory.py
new file mode 100644
index 0000000..2032f60
--- /dev/null
+++ b/Group_C/agents/DQNAgent_priority_memory.py
@@ -0,0 +1,143 @@
+from keras.layers import Dense, Flatten, Conv2D
+from keras import Sequential
+from tensorflow.keras.optimizers import Adam
+from pommerman.agents import BaseAgent
+from pommerman.agents import RandomAgent
+from pommerman import characters
+from gym.spaces import Discrete
+from Group_C.utility.replay_memory import replay_Memory
+from Group_C.utility import constants
+import numpy as np
+import tensorflow as tf
+import copy
+
+
+class DQNAgent(BaseAgent):
+    """DQN second try with keras"""
+
+    def __init__(self, character=characters.Bomber):
+        super(DQNAgent, self).__init__(character)
+        self.baseAgent = RandomAgent()
+
+        self.training_model = self.new_model()
+        self.trained_model = self.new_model()
+        self.trained_model.set_weights(self.training_model.get_weights())
+        # self.load_weights()
+
+        self.epsilon = constants.epsilon
+        self.min_epsilon = constants.MIN_EPSILON
+        self.eps_decay = constants.EPSILON_DECAY
+        self.buffer = replay_Memory(constants.MAX_BUFFER_SIZE)
+        self.update_counter = 0
+
+    def new_model(self):
+
+        model = Sequential()
+        input_shape = (constants.MINIBATCH_SIZE, 18, 11, 11,)
+        model.add(Conv2D(256, 3, (1, 1), input_shape=input_shape[1:], activation="relu", data_format="channels_first",
+                         padding="same"))
+        # print(model.output_shape)
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+        # print(model.output_shape)
+        model.add(Conv2D(256, 3, (1, 1), activation="relu", data_format="channels_first", padding="same"))
+        # print(model.output_shape)
+
+        model.add(Flatten())
+        model.add(Dense(128, activation="relu"))
+        model.add(Dense(6, activation='linear'))
+        model.compile(loss="mse", optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])
+        return model
+
+    def act(self, obs, action_space):
+        return self.baseAgent.act(obs, Discrete(6))
+
+    def train(self):
+
+        if self.buffer.size() < constants.MIN_REPLAY_MEMORY_SIZE:
+            return
+
+        current_states, action, reward, new_states, done = self.buffer.sample_element_pri(constants.MINIBATCH_SIZE)
+
+        # Take the current_states in the sample, get the Q value from the model
+        current_states_q = self.training_model.predict(current_states)
+
+        # Take next_state in the sample, get the Q value from the old network
+        new_states_q = self.trained_model.predict(new_states)
+
+        # X is the state, Y is the predicted action
+        states = []
+        actions = []
+
+        for index in range(constants.MINIBATCH_SIZE):
+
+            if done[index] is not True:
+                # Update Q value
+                new_state_q = reward[index] + constants.DISCOUNT * np.max(new_states_q[index])
+            else:
+                new_state_q = reward[index]
+
+            # Update the Q value for the given states
+            current_better_q = current_states_q[index]
+            current_better_q[action[index]] = new_state_q
+
+            # Add training data
+            states.append(current_states[index])
+            actions.append(current_better_q)
+
+        # Start training
+        self.training_model.fit(np.array(states), np.array(actions), batch_size=constants.MINIBATCH_SIZE, verbose=0,
+                                shuffle=False)
+
+        # Update network update counters
+        if done:
+            self.update_counter += 1
+
+        # Network update counter reaches upper limit, update network
+        if self.update_counter > constants.UPDATE_EVERY:
+            self.trained_model.set_weights(self.training_model.get_weights())
+            self.update_counter = 0
+
+    def calculate_td_error(self, state, action, reward, new_state, done):
+        state_ = tf.reshape(state, (-1, 18, 11, 11))
+        new_state_ = tf.reshape(new_state, (-1, 18, 11, 11))
+        q_values = self.training_model.predict(state_)[0]
+        q_value = q_values[action]
+        if not done:
+            target = reward + constants.DISCOUNT * \
+                     self.trained_model.predict(new_state_)[0, np.argmax(self.training_model.predict(new_state_))]
+        else:
+            target = reward
+
+        td_error = target - q_value
+        return td_error
+
+    def action_choose(self, state):
+
+        state_reshape = tf.reshape(state, (-1, 18, 11, 11))
+        q_table = self.training_model.predict(state_reshape)
+        if np.random.random() <= 0.001:
+            print(q_table)
+        return q_table
+
+    def save_buffer(self, state_feature, action, reward, next_state_feature, done):
+        td_error = self.calculate_td_error(state_feature, action, reward, next_state_feature, done)
+        self.buffer.append_pri(state_feature, action, reward, next_state_feature, done, td_error)
+
+    def epsilon_decay(self):
+        self.epsilon = self.epsilon * self.eps_decay if self.epsilon > self.min_epsilon else self.epsilon
+
+    def save_weights(self, numOfEpisode):
+
+        # Archive parameters after training
+        # save weight every "save_weight" episode, change it in constants.py
+        if numOfEpisode % constants.save_weight == 0:
+            self.training_model.save_weights(('./checkpoints/episode{:}/episode{:}'.format(numOfEpisode, numOfEpisode)))
+            print("weights saved!")
+
+    def load_weights(self, weight_name):
+        self.training_model.load_weights('./checkpoints/{:}/{:}'.format(weight_name, weight_name))
+        self.trained_model.load_weights('./checkpoints/{:}/{:}'.format(weight_name, weight_name))
+        print("weights loaded!")
+
+    def save_model(self, model_name):
+        self.training_model.save("./{:}".format(model_name))
diff --git a/Group_C/main_save_model.py b/Group_C/main_save_model.py
new file mode 100644
index 0000000..a39727d
--- /dev/null
+++ b/Group_C/main_save_model.py
@@ -0,0 +1,32 @@
+def main(model_name='new_model', strategy='DQN_basic'):
+    # strategies: 'DQN_basic', 'DQN_double', 'DQN_dueling', 'DQN_priority', 'DQN_noisy', 'DQN_multi_steps', 'DQN_final'
+
+    if strategy == 'DQN_basic':
+        from agents.DQNAgent_basic import DQNAgent
+    elif strategy == 'DQN_double':
+        from agents.DQNAgent_double_dqn import DQNAgent
+    elif strategy == 'DQN_dueling':
+        from agents.DQNAgent_dueling_dqn import DQNAgent
+    elif strategy == 'DQN_priority':
+        from agents.DQNAgent_priority_memory import DQNAgent
+    elif strategy == 'DQN_noisy':
+        from agents.DQNAgent_noisy import DQNAgent
+    elif strategy == 'DQN_multi_steps':
+        from agents.DQNAgent_multi_steps import DQNAgent
+    elif strategy == 'DQN_final':
+        from agents.DQNAgent_final import DQNAgent
+
+    agent1 = DQNAgent()
+
+    # load saved weights from checkpoint
+    weight = "episode100"
+    agent1.load_weights(weight)
+
+    # save your model
+    agent1.save_model(model_name)
+
+
+if __name__ == '__main__':
+    # rename your model
+    main(model_name='new_model', strategy='DQN_basic')
+    # strategies: 'DQN_basic', 'DQN_double', 'DQN_dueling', 'DQN_priority', 'DQN_noisy', 'DQN_multi_steps', 'DQN_final'
diff --git a/Group_C/main_test.py b/Group_C/main_test.py
new file mode 100644
index 0000000..ef1b6f4
--- /dev/null
+++ b/Group_C/main_test.py
@@ -0,0 +1,87 @@
+from Group_C.utility import constants
+from agents.Agent1 import DQNAgent as agent_1
+from agents.Agent3 import DQNAgent as agent_3
+from pommerman.agents import SimpleAgent
+
+import pommerman
+
+
+def main():
+    agent1 = agent_1()
+    agent2 = SimpleAgent()
+    agent3 = agent_3()
+    agent4 = SimpleAgent()
+
+    agent_list = [agent1, agent2, agent3, agent4]
+    env = pommerman.make("PommeRadioCompetition-v2", agent_list)
+    # Record average reward
+    episode_rewards = []
+    win = 0
+    draw = 0
+    total_game = 0
+
+    total_numOfSteps = 0
+    episode = 0
+    # Conduct 100 rounds of testing
+    for i in range(100):
+        current_state = env.reset()
+        # Convert state to 1D array
+        episode_reward = 0
+        numOfSteps = 0
+        episode += 1
+        done = False
+
+        while not done:
+
+            numOfSteps += 1
+            total_numOfSteps += 1
+            actions = env.act(current_state)
+            env.render()
+            new_state, result, done, info = env.step(actions)
+
+            if 10 not in new_state[0]["alive"]:
+                done = True
+
+            # Update state
+            current_state = new_state
+
+            if done:
+                break
+
+        result = 0
+
+        if done:
+            episode_rewards.append(episode_reward)
+            total_game += 1
+            if 0 in info.get('winners', []):
+                win += 1
+                result = 2
+
+        # Record wins and losses
+        if numOfSteps == constants.MAX_STEPS + 1:
+            draw += 1
+            result = 1
+
+        win_rate = win / total_game
+        draw_rate = draw / total_game
+
+        if episode % constants.SHOW_EVERY == 0:
+            if result == 1:
+                print("{} episodes done, result: {} , steps: {}, win_rate:{:.2f}, draw_rate:{:.2f}".format(episode,
+                                                                                                           'draw',
+                                                                                                           numOfSteps,
+                                                                                                           win_rate,
+                                                                                                           draw_rate))
+            else:
+                print("{} episodes done, result: {} , steps: {}, win_rate:{:.2f}, draw_rate:{:.2f}".format(episode,
+                                                                                                           'win' if result == 2 else "lose",
+                                                                                                           numOfSteps,
+                                                                                                           win_rate,
+                                                                                                           draw_rate))
+
+    print("win: ", win, " draw: ", draw)
+    env.close()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/Group_C/main_train.py b/Group_C/main_train.py
new file mode 100644
index 0000000..815b839
--- /dev/null
+++ b/Group_C/main_train.py
@@ -0,0 +1,167 @@
+from Group_C.utility import constants
+import pommerman
+import numpy as np
+import pandas as pd
+import random
+
+from pommerman.agents import SimpleAgent
+from Group_C.utility.utility import featurize2D, reward_shaping
+
+
+def main(strategy='DQN_basic'):
+    # strategies: 'DQN_basic', 'DQN_double', 'DQN_dueling', 'DQN_priority', 'DQN_noisy', 'DQN_multi_steps', 'DQN_final'
+
+    if strategy == 'DQN_basic':
+        from agents.DQNAgent_basic import DQNAgent
+    elif strategy == 'DQN_double':
+        from agents.DQNAgent_double_dqn import DQNAgent
+    elif strategy == 'DQN_dueling':
+        from agents.DQNAgent_dueling_dqn import DQNAgent
+    elif strategy == 'DQN_priority':
+        from agents.DQNAgent_priority_memory import DQNAgent
+    elif strategy == 'DQN_noisy':
+        from agents.DQNAgent_noisy import DQNAgent
+    elif strategy == 'DQN_multi_steps':
+        from agents.DQNAgent_multi_steps import DQNAgent
+    elif strategy == 'DQN_final':
+        from agents.DQNAgent_final import DQNAgent
+
+    agent1 = DQNAgent()
+    agent2 = SimpleAgent()
+    agent3 = SimpleAgent()
+    agent4 = SimpleAgent()
+
+    agent_list = [agent1, agent2, agent3, agent4]
+
+    env = pommerman.make('PommeFFACompetitionFast-v0', agent_list)
+    # Record average reward
+    episode_rewards = []
+
+    win = 0
+    draw = 0
+    total_game = 0
+    reward_to_csv = []
+    result_to_csv = []
+
+    total_numOfSteps = 0
+    episode = 0
+
+    """please stop training manually"""
+    while True:
+
+        current_state = env.reset()
+        # Convert state to 1D array
+        episode_reward = 0
+        numOfSteps = 0
+        episode += 1
+        done = False
+
+        while not done:
+
+            state_feature = featurize2D(current_state[2])
+            numOfSteps += 1
+            total_numOfSteps += 1
+            # Use random action to collect data
+            if constants.epsilon > np.random.random() and total_numOfSteps >= constants.MIN_REPLAY_MEMORY_SIZE:
+                # Get Action
+                actions = env.act(current_state)
+                actions[0] = np.argmax(agent1.action_choose(state_feature)).tolist()
+            else:
+                # Use random action collects data
+                actions = env.act(current_state)
+                actions[0] = random.randint(0, 5)
+
+            new_state, result, done, info = env.step(actions)
+
+            # If our agent is dead, the game is stopped and we accelerate training
+            if 10 not in new_state[0]["alive"]:
+                done = True
+
+            # reward_shaping
+            agent1.buffer.append_action(actions[0])
+            reward = reward_shaping(current_state[0], new_state[0], actions[0], result[0], agent1.buffer.buffer_action)
+
+            next_state_feature = featurize2D(new_state[0])
+            episode_reward += reward
+
+            # Display the game screen for each set number of games
+            if constants.SHOW_PREVIEW and not episode % constants.SHOW_GAME:
+                env.render()
+
+            # Store memory
+            agent1.save_buffer(state_feature, actions[0], reward, next_state_feature, done)
+            # Learn
+            agent1.train()
+            # Update state
+            current_state = new_state
+
+            if done:
+                break
+
+        result = 0
+
+        if done:
+            episode_rewards.append(episode_reward)
+            total_game += 1
+            if 0 in info.get('winners', []):
+                win += 1
+                result = 2
+
+        # Record win and losses
+        if numOfSteps == constants.MAX_STEPS + 1:
+            draw += 1
+            result = 1
+        win_rate = win / total_game
+        draw_rate = draw / total_game
+
+        # Store reward
+        reward_to_csv.append(episode_reward)
+        # Store result
+        result_to_csv.append(result)
+
+        if episode % constants.SHOW_EVERY == 0:
+            if result == 1:
+                print("{} episodes done, result: {} , steps: {}".format(episode,
+                                                                        'draw',
+                                                                        numOfSteps))
+
+                print("Reward {:.2f}, Average Episode Reward: {:.3f}, win_rate:{:.2f}, draw_rate:{:.2f}".format(
+                    episode_reward,
+                    np.mean(episode_rewards),
+                    win_rate,
+                    draw_rate))
+            else:
+                print("{} episodes done, result: {} , steps: {}".format(episode,
+                                                                        'win' if result == 2 else "lose",
+                                                                        numOfSteps))
+
+                print("Reward {:.3f}, Average Episode Reward: {:.3f}, win_rate:{:.2f}, draw_rate:{:.2f}".format(
+                    episode_reward,
+                    np.mean(episode_rewards),
+                    win_rate,
+                    draw_rate))
+
+        agent1.epsilon_decay()
+        agent1.save_weights(episode)
+
+        # function for data augmentation
+        # agent1.data_processing(numOfSteps, episode_reward, result, episode)
+
+        """If you want to save result and reward as csv, please uncomment the code below"""
+        # Record the results and chart them
+        # if episode % 50 == 0:
+        #     df_reward = pd.DataFrame({"reward": reward_to_csv})
+        #     df_reward.to_csv("reward.csv", index=False, mode="a", header=False)
+        #     print("successfully saved reward")
+        #     reward_to_csv = []
+        #     df_result = pd.DataFrame({"result": result_to_csv})
+        #     df_result.to_csv("result.csv", index=False, mode="a", header=False)
+        #     print("successfully saved result")
+        #     result_to_csv = []
+
+    env.close()
+
+
+if __name__ == '__main__':
+    main(strategy='DQN_double')
+    # strategies: 'DQN_basic', 'DQN_double', 'DQN_dueling', 'DQN_priority', 'DQN_noisy', 'DQN_multi_steps', 'DQN_final'
diff --git a/Group_C/requirements.txt b/Group_C/requirements.txt
new file mode 100644
index 0000000..e519b44
--- /dev/null
+++ b/Group_C/requirements.txt
@@ -0,0 +1,24 @@
+docker~=3.1
+gym~=0.10.5
+scipy~=1.0
+Pillow~=8.2.0
+ruamel.yaml~=0.15
+Flask~=1.1.2
+requests~=2.18
+jsonmerge~=1.5.1
+astroid>=2
+isort~=4.3.4
+pylint>=2
+websockets~=6.0
+websocket-client~=0.53.0
+python-cli-ui~=0.7.1
+python-rapidjson~=0.6.3
+Click~=7.1.2
+
+numpy~=1.20.1
+pandas~=1.2.4
+setuptools~=52.0.0
+tensorflow~=2.6.0
+keras~=2.6.0
+pyglet~=1.5.21
+matplotlib~=3.5.1
\ No newline at end of file
diff --git a/Group_C/result_image/Reward_rainbow.png b/Group_C/result_image/Reward_rainbow.png
new file mode 100644
index 0000000..9482d67
Binary files /dev/null and b/Group_C/result_image/Reward_rainbow.png differ
diff --git a/Group_C/result_image/data/00reward basic DQN.csv b/Group_C/result_image/data/00reward basic DQN.csv
new file mode 100644
index 0000000..71d7ef0
--- /dev/null
+++ b/Group_C/result_image/data/00reward basic DQN.csv	
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diff --git a/Group_C/result_image/data/01reward basic DQN with data augmentation.csv b/Group_C/result_image/data/01reward basic DQN with data augmentation.csv
new file mode 100644
index 0000000..5deee5a
--- /dev/null
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diff --git a/Group_C/result_image/data/02reward double DQN.csv b/Group_C/result_image/data/02reward double DQN.csv
new file mode 100644
index 0000000..48ee8e4
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diff --git a/Group_C/result_image/data/03reward dueling DQN.csv b/Group_C/result_image/data/03reward dueling DQN.csv
new file mode 100644
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--- /dev/null
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diff --git a/Group_C/result_image/data/04reward noisy network.csv b/Group_C/result_image/data/04reward noisy network.csv
new file mode 100644
index 0000000..282dcc6
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diff --git a/Group_C/result_image/data/05reward priority memory.csv b/Group_C/result_image/data/05reward priority memory.csv
new file mode 100644
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diff --git a/Group_C/result_image/data/06reward Multi-step DQN.csv b/Group_C/result_image/data/06reward Multi-step DQN.csv
new file mode 100644
index 0000000..60b0462
--- /dev/null
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diff --git a/Group_C/result_image/data/07reward double + priority.csv b/Group_C/result_image/data/07reward double + priority.csv
new file mode 100644
index 0000000..d710603
--- /dev/null
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diff --git a/Group_C/result_image/data/08reward rainbow DQN.csv b/Group_C/result_image/data/08reward rainbow DQN.csv
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diff --git a/Group_C/result_image/make_image.py b/Group_C/result_image/make_image.py
new file mode 100644
index 0000000..d7999d9
--- /dev/null
+++ b/Group_C/result_image/make_image.py
@@ -0,0 +1,95 @@
+# -*- coding: utf-8 -*-
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import csv
+
+
+def plot(name_png, title_graph, list_xy, list_label, range_epi):
+    """
+    make a line chart by all the csv from data and save it
+    input:
+        name_png: name of the diagram
+        title_graph: title of the diagram
+        List_label:  labels of the line ，Format：['label1', 'label2', ....]
+    """
+    list_data = load_csv()
+    # list_maker = [',', '.', 'o', 'v', '^', '<', '>', '+', 'x', 'p', '*']
+    list_maker = [',', ',', ',', ',', ',', ',', ',', ',', ',', 'p', '*']
+    if len(list_data) != 0:
+        plt.figure(1)
+        plt.title(title_graph)
+        plt.xlabel(list_xy[0])
+        plt.ylabel(list_xy[1])
+        for index, list_d in enumerate(list_data):
+            s = len(list_d)
+            x = [i + 1 for i in range(s)]
+            x, y = epi_display_range(x, list_d, range_epi)
+            plt.plot(x, y, label=list_label[index], marker=list_maker[index],
+                     alpha=0.9, linewidth=1)
+
+    # plt.gca().yaxis.set_major_locator(MultipleLocator(int(len(List_data[0]) / 10)))
+    if len(list_label) > 1:
+        plt.legend(loc='upper left', fontsize='xx-small', frameon=False)
+    png_path = get_path(name_png, 'graphs')
+    plt.savefig(png_path)
+    plt.show()
+
+
+def load_csv():
+    # load all the csv
+    check_folder(['graphs'])
+    path_folder = os.path.join(os.getcwd().replace('\\', '/'), 'csv')
+    dirs = os.listdir(path_folder)
+    dirs.sort(key=lambda x: int(x[:-11]))
+    list_data = []
+    if len(dirs) != 0:
+        for d in dirs:
+            path_csv = os.path.join(path_folder, d)
+            file = open(path_csv)
+            reader = csv.reader(file)
+            data = list(reader)
+            data = np.array(data)
+            data = data.astype(float).tolist()
+            average_data = sum_average(data)
+            # list_data.append([r[col] for r in data for col in range(len(data[0]))])
+            list_data.append(average_data)
+    return list_data
+
+
+def get_path(name, name_floder):
+    folder_path = os.getcwd().replace('\\', '/')
+    png_path = os.path.join(folder_path, name_floder, name)
+    return png_path
+
+
+def check_folder(list_name):
+    current_path = os.getcwd().replace('\\', '/')
+    if len(list_name) != 0:
+        for name in list_name:
+            path_folder = os.path.join(current_path, name)
+            if not os.path.exists(path_folder):
+                os.mkdir(path_folder)
+
+
+def sum_average(list_data):
+    list_data_ave = []
+    sum_data = 0
+    average = []
+    for index, data in enumerate(list_data):
+        sum_data += data[0]
+        average.append(sum_data / (index + 1))
+    return average
+
+
+def epi_display_range(x, y, range_epi):
+    return x[range_epi[0]:range_epi[1]], y[range_epi[0]:range_epi[1]]
+
+
+if __name__ == '__main__':
+    # plot('name_png', 'title_graph', ['episodes', 'result'], ['result', 'reward'], [100, 800])
+    plot('Reward_rainbow', 'Rainbow DQN', ['Episodes', 'Average reward'],
+         ['DQN', 'DQN with data augmentation', 'Double DQN', 'Dueling DQN',
+          'DQN + noisy network', 'DQN + priority memory', 'Multi-step DQN',
+          'Double DQN + priority', 'Rainbow DQN'], [50, 800])
+    # plot('Reward_DQN + priority memory', 'DQN + priority memory', ['episodes', 'reward'], ['Basic DQN'])
diff --git a/Group_C/utility/action_filter.py b/Group_C/utility/action_filter.py
new file mode 100644
index 0000000..24c16be
--- /dev/null
+++ b/Group_C/utility/action_filter.py
@@ -0,0 +1,302 @@
+import random
+
+import numpy as np
+
+
+def action_filter(current_state, action_list):
+    """Filtrate the action to improve survival rate"""
+    X = current_state["position"][0]
+    Y = current_state["position"][1]
+    # Record the movable positions around the agent
+    move_list = make_move_list(current_state, X, Y)
+    # If agent cannot move, then excute action 0 (waiting to die)
+    if move_list is None:
+        return 0
+    # Get the moveable position from agent after statistical analysing various actions
+    moveable_position = make_move_position(move_list, X, Y, action_list)
+    # Statistical analysing all bombs in visual range
+    bomb_list = make_bomb_list(current_state)
+    # Grade the danger level of the new location
+    moveable_position_score = make_dangerous_list(current_state, moveable_position, bomb_list, action_list)
+
+    actions = []
+    # Use a safe location if one exists
+    for action in moveable_position_score:
+        if action[3] == 0:
+            actions.append(action[2])
+    # If a original action is safe, directly choose the original action
+    if action_list[-1] in actions:
+        return action_list[-1]
+
+    # If there is no safe position, choose the position of risk degree 1
+    if actions is None:
+        actions = []
+        for action in moveable_position_score:
+            if action[3] == 1:
+                actions.append(action[2])
+    # If a original action is safe, directly choose the original action
+    if action_list[-1] in actions:
+        return action_list[-1]
+
+    # If there is no position of risk degree 1, directly choose the position of risk degree 2
+    if actions is None:
+        actions = []
+        for action in moveable_position_score:
+            if action[3] == 2:
+                actions.append(action[2])
+
+    # If there is no position of risk degree 2, choose the position of risk degree 3
+    if actions is None:
+        actions = []
+        for action in moveable_position_score:
+            if action[3] == 3:
+                actions.append(action[2])
+
+    if actions is None:
+        return random.randint(0, 5)
+
+    # If multiple actions are available, the non-0 action is preferred
+    if len(actions) != 0:
+        modified_action = [action for action in actions if action > 0]
+        if len(modified_action) > 0:
+            modified_action = random.sample(actions, 1)
+            return modified_action[0]
+        else:
+            return 0
+
+    else:
+        return action_list[-1]
+
+
+def make_move_list(current_state, X, Y):
+    """Check if the action is executable (meaningful)"""
+
+    def check_bomb_action(current_state):
+        """Check if Action 5 makes sense"""
+        meaningful = False
+        X = current_state["position"][0]
+        Y = current_state["position"][1]
+        blast_strength = current_state["blast_strength"]
+        # Determine if there's a wall to the left of the bomb
+        for strength in range(1, blast_strength):
+            # Check if an agent is outside the map boundaries
+            if Y - strength < 0:
+                break
+            # If it is rigid, break
+            elif current_state["board"][X][Y - strength] == 1:
+                break
+            # Check if there is wood, then set meaningful to true
+            elif current_state["board"][X][Y - strength] == 2:
+                meaningful = True
+                return meaningful
+            # It makes sense if there's an enemy in the blast range
+            elif current_state["board"][X][Y - strength] in [10, 11, 12, 13]:
+                meaningful = True
+                return meaningful
+
+        # Determine if there's a wall to the right of the bomb
+        for strength in range(1, blast_strength):
+            # Check if an agent is outside the map boundaries
+            if Y + strength > 10:
+                break
+                # If it is rigid, break
+            elif current_state["board"][X][Y + strength] == 1:
+                break
+            # Check if there is wood, then set meaningful to true
+            elif current_state["board"][X][Y + strength] == 2:
+                meaningful = True
+                return meaningful
+            # It makes sense if there's an enemy in the blast range
+            elif current_state["board"][X][Y + strength] in [10, 11, 12, 13]:
+                meaningful = True
+                return meaningful
+
+        # Determine if there is a wall above the bomb
+        for strength in range(1, blast_strength):
+            # Check if an agent is outside the map boundaries
+            if X - strength < 0:
+                break
+            # If it is rigid, break
+            elif current_state["board"][X - strength][Y] == 1:
+                break
+            # Check if there is wood, then set meaningful to true
+            elif current_state["board"][X - strength][Y] == 2:
+                meaningful = True
+                return meaningful
+            # It makes sense if there's an enemy in the blast range
+            elif current_state["board"][X - strength][Y] in [10, 11, 12, 13]:
+                meaningful = True
+                return meaningful
+
+        # Determine if there is a wall under the bomb
+        for strength in range(1, blast_strength):
+            # Check if an agent is outside the map boundaries
+            if X + strength > 10:
+                break
+            # If it is rigid, break
+            elif current_state["board"][X + strength][Y] == 1:
+                break
+            # Check if there is wood, then set meaningful to true
+            elif current_state["board"][X + strength][Y] == 2:
+                meaningful = True
+                return meaningful
+            # It makes sense if there's an enemy in the blast range
+            elif current_state["board"][X + strength][Y] in [10, 11, 12, 13]:
+                meaningful = True
+                return meaningful
+        return meaningful
+
+    def check_moveable(current_state, X, Y):
+        """Check whether an agent can be moved in this position"""
+        moveable = False
+        if X < 0 or X > 10 or Y < 0 or Y > 10:
+            return moveable
+        elif current_state["board"][X][Y] in [0, 4, 6, 7, 8]:
+            moveable = True
+            return moveable
+        elif current_state["board"][X][Y] == 3 and current_state["can_kick"] is True:
+            moveable = True
+            return moveable
+
+        return moveable
+
+    move_list = [0]
+    can_up = check_moveable(current_state, X - 1, Y)
+    if can_up:
+        move_list.append(1)
+    can_down = check_moveable(current_state, X + 1, Y)
+    if can_down:
+        move_list.append(2)
+    can_left = check_moveable(current_state, X, Y - 1)
+    if can_left:
+        move_list.append(3)
+    can_right = check_moveable(current_state, X, Y + 1)
+    if can_right:
+        move_list.append(4)
+    if current_state["ammo"] != 0 and check_bomb_action(current_state):
+        move_list.append(5)
+    return move_list
+
+
+def make_move_position(move_list, X, Y, action_list):
+    """Calculate risk degree of the movable position"""
+    # First two columns, post movement position, third column, movement, fourth column, risk degree
+    moveable_position = []
+
+    for action in move_list:
+        if action == 0:
+            moveable_position.append([X, Y, 0, 0])
+        elif action == 1:
+            moveable_position.append([X - 1, Y, 1, 0])
+        elif action == 2:
+            moveable_position.append([X + 1, Y, 2, 0])
+        elif action == 3:
+            moveable_position.append([X, Y - 1, 3, 0])
+        elif action == 4:
+            moveable_position.append([X, Y + 1, 4, 0])
+        elif action == 5:
+            if action_list[-2] != 5:
+                moveable_position.append([X, Y, 5, 0])
+    return moveable_position
+
+
+def make_bomb_list(current_state):
+    bomb_list = []
+    for X in range(11):
+        for Y in range(11):
+            if current_state["board"][X][Y] == 3:
+                bomb_list.append([X, Y])
+
+    return bomb_list
+
+
+def make_dangerous_list(current_state, moveable_position, bomb_list, action_list):
+    # safe 0 , dangerous 1, high_dangerous 2, death 3.
+
+    def check_block(current_state, position_agent, position_bomb):
+        # Check for obstacles between mines and agents
+        block = False
+        if position_agent[0] != position_bomb[0]:
+            for index in range(1, abs(position_agent[0] - position_bomb[0])):
+                # Check for wood and rigid
+                if current_state["board"][min(position_agent[0], position_bomb[0]) + index][position_agent[1]] in [1,
+                                                                                                                   2]:
+                    block = True
+                    break
+        elif position_agent[1] != position_bomb[1]:
+            for index in range(1, abs(position_agent[1] - position_bomb[1])):
+                # Check for wood and rigid
+                if current_state["board"][position_agent[0]][min(position_agent[1], position_bomb[1]) + index] in [1,
+                                                                                                                   2]:
+                    block = True
+                    break
+
+        return block
+
+    # Check dead ends
+    def check_dead_end(current_state, moveable_position, action_list):
+        for position in moveable_position:
+            if action_list[-2] == 5 and position[2] == 1:
+                if (position[1] - 1 < 0 or current_state["board"][position[0]][position[1] - 1] in [1, 2, 3]) and \
+                        (position[1] + 1 > 10 or current_state["board"][position[0]][position[1] + 1] in [1, 2, 3]) and \
+                        (position[0] - 1 < 0 or current_state["board"][position[0] - 1][position[1]] in [1, 2, 3]):
+                    position[-1] = 3
+
+            elif action_list[-2] == 5 and position[2] == 2:
+                if (position[1] - 1 < 0 or current_state["board"][position[0]][position[1] - 1] in [1, 2, 3]) and \
+                        (position[1] + 1 > 10 or current_state["board"][position[0]][position[1] + 1] in [1, 2, 3]) and \
+                        (position[0] + 1 > 10 or current_state["board"][position[0] + 1][position[1]] in [1, 2, 3]):
+                    position[-1] = 3
+
+            elif action_list[-2] == 5 and position[2] == 3:
+                if (position[0] - 1 < 0 or current_state["board"][position[0] - 1][position[1]] in [1, 2, 3]) and \
+                        (position[1] - 1 < 0 or current_state["board"][position[0]][position[1] - 1] in [1, 2, 3]) and \
+                        (position[0] + 1 > 10 or current_state["board"][position[0] + 1][position[1]] in [1, 2, 3]):
+                    position[-1] = 3
+
+            elif action_list[-2] == 5 and position[2] == 4:
+                if (position[0] - 1 < 0 or current_state["board"][position[0] - 1][position[1]] in [1, 2, 3]) and \
+                        (position[1] + 1 > 10 or current_state["board"][position[0]][position[1] + 1] in [1, 2, 3]) and \
+                        (position[0] + 1 > 10 or current_state["board"][position[0] + 1][position[1]] in [1, 2, 3]):
+                    position[-1] = 3
+
+        return moveable_position
+
+    # Check the flame
+    def check_flame(current_state, moveable_position):
+        for position in moveable_position:
+            if current_state["flame_life"][position[0]][position[1]] != 0:
+                position[-1] = 3
+        return moveable_position
+
+    # bomb_location_all = np.where(np.array(current_state["board"]) == 3)
+    # if len(bomb_location_all[0]) == 0:
+    #     return moveable_position
+    moveable_position = check_dead_end(current_state, moveable_position, action_list)
+
+    # Check for flames, even if there is no bomb in sight
+    if len(bomb_list) == 0:
+        moveable_position = check_flame(current_state, moveable_position)
+        return moveable_position
+        '''
+            Moveable position is classified according to the danger level.
+            when agent is threatened by multiple bombs or flames in the same location,
+            the highest danger level is recorded first.
+        '''
+    for agent_position in moveable_position:
+        for bomb_position in bomb_list:
+            if bomb_position[0] == agent_position[0] or bomb_position[1] == agent_position[1]:
+                if check_block(current_state, agent_position, bomb_position) is False and abs(
+                        agent_position[0] - bomb_position[0]) + abs(agent_position[1] - bomb_position[1]) <= \
+                        (current_state["bomb_blast_strength"][bomb_position[0]][bomb_position[1]] - 1):
+                    if current_state["flame_life"][agent_position[0]][agent_position[1]] >= 1 or \
+                            current_state["bomb_life"][bomb_position[0]][bomb_position[1]] == 1:
+                        agent_position[3] = 3
+                    elif 1 < current_state["bomb_life"][bomb_position[0]][bomb_position[1]] <= 3:
+                        if agent_position[3] < 2:
+                            agent_position[3] = 2
+                    elif 4 <= current_state["bomb_life"][bomb_position[0]][bomb_position[1]]:
+                        if agent_position[3] < 1:
+                            agent_position[3] = 1
+
+    return moveable_position
diff --git a/Group_C/utility/communication.py b/Group_C/utility/communication.py
new file mode 100644
index 0000000..206062c
--- /dev/null
+++ b/Group_C/utility/communication.py
@@ -0,0 +1,67 @@
+import numpy as np
+
+
+def message(current_state):
+    """
+    FFA: message = the maximum distance bomb coordinate with the agent
+    Radio: message = bomb coordinates 4-7 away from teammates,
+                     Or the coordinates of the bomb with the maximum distance from teammates,
+                     Or the maximum distance bomb coordinate with the agent
+    input: current_state
+    output: message, current_state processed using the received message
+    """
+
+    position_agent = current_state["position"]
+    board = current_state["board"]
+    teammate = current_state["teammate"].value
+    list_position_bomb = np.argwhere(board == 3).tolist()
+    position_teammate = np.argwhere(board == teammate).tolist()
+    r_mess = current_state['message']
+
+    # Correct the inverted coordinates and Ignore bombs on the edge
+    if len(list_position_bomb) != 0:
+        for index, position_bomb in enumerate(list_position_bomb):
+            position_bomb_ = [position_bomb[1], position_bomb[0]]
+            list_position_bomb[index] = position_bomb_
+            if position_bomb[0] < 1 or position_bomb[0] > 9 or \
+                    position_bomb[1] < 1 or position_bomb[1] > 9:
+                list_position_bomb.pop(index)
+
+    # Add the received bomb position to the current state
+    if r_mess != (0, 0):
+        current_state["board"][r_mess[0]+1, r_mess[1]+1] = 3
+
+    if teammate == 9 or len(position_teammate) == 0:
+        if len(list_position_bomb) != 0:
+            return position_max_dis(position_agent, list_position_bomb), current_state
+        else:
+            return [0, 0], current_state
+    else:
+        if len(list_position_bomb) != 0:
+            return position_max_dis_limit(position_teammate[0], list_position_bomb), current_state
+        else:
+            return [0, 0], current_state
+
+
+def distance(x, y):
+    return np.linalg.norm(np.array(x) - np.array(y))
+
+
+def position_max_dis(x, list_y):
+    list_distance = []
+    for y in list_y:
+        list_distance.append(distance(x, y))
+    return [list_y[np.argmax(np.array(list_distance))][0] - 1, list_y[np.argmax(np.array(list_distance))][1] - 1]
+
+
+def position_max_dis_limit(x, list_y):
+    limit = [4, 7]
+    list_distance = []
+    for y in list_y:
+        dis = distance(x, y)
+        if limit[0] < dis < limit[1]:
+            list_distance.append(dis)
+    if len(list_distance) != 0:
+        return [list_y[np.argmax(np.array(list_distance))][0] - 1, list_y[np.argmax(np.array(list_distance))][1] - 1]
+    else:
+        return [0, 0]
\ No newline at end of file
diff --git a/Group_C/utility/constants.py b/Group_C/utility/constants.py
new file mode 100644
index 0000000..4b25f60
--- /dev/null
+++ b/Group_C/utility/constants.py
@@ -0,0 +1,26 @@
+from pommerman import constants
+
+MIN_REPLAY_MEMORY_SIZE = 1000  # Minimum number of steps in a memory to start training
+MINIBATCH_SIZE = 128  # How many steps (samples) to use for training
+UPDATE_EVERY = 10  # Terminal states (end of episodes)
+MAX_BUFFER_SIZE = 100_000
+MAX_BUFFER_SIZE_PRE = 1_000_000
+DISCOUNT = 0.95
+MAX_STEPS = constants.MAX_STEPS
+n_step = 5
+
+Threshold_min = -0.40 / 100
+Threshold_max = -0.20 / 300
+
+# Environment settings
+EPISODES = 100000
+SHOW_EVERY = 1
+
+# Exploration settings
+epsilon = 1  # not a constant, going to be decayed
+EPSILON_DECAY = 0.99975
+MIN_EPSILON = 0.95
+
+SHOW_PREVIEW = True
+SHOW_GAME = 100
+save_weight = 100
diff --git a/Group_C/utility/replay_memory.py b/Group_C/utility/replay_memory.py
new file mode 100644
index 0000000..852244a
--- /dev/null
+++ b/Group_C/utility/replay_memory.py
@@ -0,0 +1,116 @@
+import random
+import collections
+import numpy as np
+from Group_C.utility import constants
+
+
+class replay_Memory():
+    def __init__(self, MAX_BUFFER_SIZE):
+        self.n_step = constants.n_step
+        self.buffer = collections.deque(maxlen=MAX_BUFFER_SIZE)
+        self.n_step_buffer = collections.deque(maxlen=self.n_step)
+        self.buffer_episode = collections.deque()
+        self.buffer_processing = collections.deque()
+        self.buffer_action = collections.deque([0, 0, 0, 0], maxlen=4)
+        self.buffer_td = collections.deque(maxlen=MAX_BUFFER_SIZE)
+        self.alpha = 0.6
+        self.gamma = 0.9  # affinity for long term reward
+
+    def append(self, transition):
+        self.buffer.append(transition)
+
+    def append_n_step(self, state, action, reward, next_state, done):
+        # multi steps DQN
+        self.n_step_buffer.append((state, action, reward, next_state, done))
+        if len(self.n_step_buffer) < self.n_step:
+            return
+        l_reward, l_next_state, l_done = self.n_step_buffer[-1][-3:]
+        for transition in reversed(list(self.n_step_buffer)[:-1]):
+            r, n_s, d = transition[-3:]
+            l_reward = r + self.gamma * l_reward * (1 - d)
+            l_next_state, l_done = (n_s, d) if d else (l_next_state, l_done)
+        l_state, l_action = self.n_step_buffer[0][:2]
+        transition_ = (l_state, l_action, l_reward, l_next_state, l_done)
+        self.buffer.append(transition_)
+
+    def append_action(self, action):
+        self.buffer_action.append(action)
+
+    def append_augmentation(self, transition):
+        self.buffer_episode.append(transition)
+
+    def append_processing(self, transition):
+        self.buffer_processing.append(transition)
+
+    def append_pri(self, state, action, reward, next_state, done, td_error):
+        # pri DQN
+        transition = (state, action, reward, next_state, done)
+        self.append_td(td_error)
+        self.buffer.append(transition)
+
+    def append_td(self, td_error):
+        self.buffer_td.append(td_error)
+
+    def clear(self):
+        self.buffer_episode.clear()
+        self.buffer_processing.clear()
+
+    def merge(self):
+        for element in self.buffer_processing:
+            self.buffer.append(element)
+
+    def merge_negative(self):
+        for element in self.buffer_processing:
+            if element[2] < 0:
+                self.buffer.append(element)
+
+    def sample(self, batch):
+        mini_batch = random.sample(self.buffer, batch)
+        return mini_batch
+
+    def sample_element(self, batch):
+        mini_batch = random.sample(self.buffer, batch)
+        current_state, action, reward, new_states, done = [], [], [], [], []
+
+        for transition in mini_batch:
+            curr_state, act, r, new_state, d = transition
+            current_state.append(curr_state)
+            action.append(act)
+            reward.append(r)
+            new_states.append(new_state)
+            done.append(d)
+
+        return np.array(current_state), action, reward, np.array(new_states), done
+
+    def sample_element_pri(self, batch_size):
+        # Prioritized DQN
+        # According to td_error sort, find the index, from small to large
+        index = np.argsort(np.array(self.buffer_td).flatten()).tolist()
+        # Buffers are sorted by index
+        buffer_sort = self.buffer
+        if len(index) != 0 and len(buffer_sort) != 0:
+            for i in range(len(self.buffer)):
+                buffer_sort[i] = self.buffer[index[i]]
+        prioritization = int(batch_size * self.alpha)  # self.alpha = 0.6
+        batch_prioritized = []
+        for i in range(prioritization):
+            # Add in reverse, from large to small
+            batch_prioritized.append(buffer_sort[-i - 1])
+        mini_batch = random.sample(self.buffer, batch_size - prioritization)
+        td = self.buffer_td
+        # Training uses data: batch= batch_prioritized(according to td_error to go from large to small)+mini_batch(random selection)
+        batch = batch_prioritized + mini_batch
+        current_state, action, reward, new_states, done, td_error = [], [], [], [], [], []
+
+        for transition in batch:
+            curr_state, act, r, new_state, d = transition
+            current_state.append(curr_state)
+            action.append([act])
+            reward.append([r])
+            new_states.append(new_state)
+            done.append([d])
+
+        return np.array(current_state), action, reward, np.array(new_states), done
+
+    def size(self):
+        return len(self.buffer)
diff --git a/Group_C/utility/utility.py b/Group_C/utility/utility.py
new file mode 100644
index 0000000..b2958e6
--- /dev/null
+++ b/Group_C/utility/utility.py
@@ -0,0 +1,550 @@
+import numpy as np
+
+
+def reward_shaping(current_state, new_state, action, result, action_list):
+
+    r_lose = -1
+
+    r_wood = 0.01
+    r_powerup = 0.05
+    r_kick = 0.02
+
+    r_lay_bomb = -0.005
+    r_lay_bomb_near_enemy = 0.2
+    r_attack_teammate = -0.1
+    r_get_away_from_bomb = 0.005
+    r_get_close_to_bomb = -0.01
+
+    r_avoid = 0.001
+    r_move = 0.001
+    r_stay = -0.003
+    r_move_towards_wood = -0.01
+    r_move_loop = -0.005
+    r_dead_end = -0.1
+    r_ignore_penalty = -0.0015
+
+    reward = 0
+
+    X = current_state["position"][0]
+    Y = current_state["position"][1]
+    new_X = new_state["position"][0]
+    new_Y = new_state["position"][1]
+
+    # Upper left corner when training, states of teammate and enemy
+    enemies = [11, 12, 13]
+    teammate = [10]
+
+    # Record the situation of the upper, lower, left and right grids of agent to facilitate the calculation of rewards
+    current_grids = []
+    if X - 1 >= 0:
+        current_grids.append(current_state["board"][X - 1][Y])
+    if X + 1 <= 10:
+        current_grids.append(current_state["board"][X + 1][Y])
+    if Y - 1 >= 0:
+        current_grids.append(current_state["board"][X][Y - 1])
+    if Y + 1 <= 10:
+        current_grids.append(current_state["board"][X][Y + 1])
+
+    # Check to see if the bomb was kicked
+    if current_state["can_kick"] is True and new_state["can_kick"] is False:
+        reward += r_kick
+
+    """
+        If the action value is 0, all rewards are counted.
+        Check_avoid_flame: Checks if action is intended to avoid flames
+        Check_corner_bomb: Checks if the action is used to avoid bombs
+        Check_in_flame: Checks whether the action causes agent death
+        Check_and_away_from_bomb: Checks whether the action is that agent away from the bomb
+    """
+    if action == 0:
+        reward = check_avoid_flame(reward, r_avoid, current_grids)
+        reward = check_corner_bomb(current_state, X, Y, reward, r_avoid, r_stay, current_grids)
+        reward = check_in_flame(current_state, new_state, reward, r_lose, X, Y, new_X, new_Y)
+        reward = check_and_away_from_bomb(current_state, X, Y, new_X, new_Y, reward, r_get_away_from_bomb,
+                                          r_get_close_to_bomb, action_list)
+        return reward
+
+    """
+        When executing the Move Action, collect rewards.
+        Check_dead_end: Checks whether an action is causing a dead end
+        Check_ignore_powerup: Checks if action ignores powerup (passing but not picking up)
+        Check_move_loop: Checks if an action is a meaningless operation
+        Check_power_up: Checks whether the action picks up powerUp
+        Check_in_flame: Checks whether the action causes agent death
+        Check_and_away_from_bomb: Checks whether the action is that agent away from the bomb
+        """
+    if action == 1:
+        # Check if agent hits a wall
+        if current_state["position"] == new_state["position"]:
+            reward += r_move_towards_wood
+        else:
+            reward += r_move
+            reward = check_dead_end(new_state, new_X, new_Y, action_list, reward, r_dead_end)
+            reward = check_ignore_powerup(current_state, new_X, new_Y, action_list, current_grids, reward,
+                                          r_ignore_penalty)
+        reward = check_move_loop(action_list, reward, r_move_loop)
+        reward = check_power_up(new_X, new_Y, current_state, reward, r_powerup)
+        reward = check_in_flame(current_state, new_state, reward, r_lose, X, Y, new_X, new_Y)
+        reward = check_and_away_from_bomb(current_state, X, Y, new_X, new_Y, reward, r_get_away_from_bomb,
+                                          r_get_close_to_bomb, action_list)
+        return reward
+
+    if action == 2:
+        # Check if agent hits a wall
+        if current_state["position"] == new_state["position"]:
+            reward += r_move_towards_wood
+        else:
+            reward += r_move
+            reward = check_dead_end(new_state, new_X, new_Y, action_list, reward, r_dead_end)
+            reward = check_ignore_powerup(current_state, new_X, new_Y, action_list, current_grids, reward,
+                                          r_ignore_penalty)
+        reward = check_move_loop(action_list, reward, r_move_loop)
+        reward = check_power_up(new_X, new_Y, current_state, reward, r_powerup)
+        reward = check_in_flame(current_state, new_state, reward, r_lose, X, Y, new_X, new_Y)
+        reward = check_and_away_from_bomb(current_state, X, Y, new_X, new_Y, reward, r_get_away_from_bomb,
+                                          r_get_close_to_bomb, action_list)
+        return reward
+
+    if action == 3:
+        # Check if agent hits a wall
+        if current_state["position"] == new_state["position"]:
+            reward += r_move_towards_wood
+        else:
+            reward += r_move
+            reward = check_dead_end(new_state, new_X, new_Y, action_list, reward, r_dead_end)
+            reward = check_ignore_powerup(current_state, new_X, new_Y, action_list, current_grids, reward,
+                                          r_ignore_penalty)
+        reward = check_move_loop(action_list, reward, r_move_loop)
+        reward = check_power_up(new_X, new_Y, current_state, reward, r_powerup)
+        reward = check_in_flame(current_state, new_state, reward, r_lose, X, Y, new_X, new_Y)
+        reward = check_and_away_from_bomb(current_state, X, Y, new_X, new_Y, reward, r_get_away_from_bomb,
+                                          r_get_close_to_bomb, action_list)
+        return reward
+
+    if action == 4:
+        # Check if agent hits a wall
+        if current_state["position"] == new_state["position"]:
+            reward += r_move_towards_wood
+        else:
+            reward += r_move
+            reward = check_dead_end(new_state, new_X, new_Y, action_list, reward, r_dead_end)
+            reward = check_ignore_powerup(current_state, new_X, new_Y, action_list, current_grids, reward,
+                                          r_ignore_penalty)
+        reward = check_move_loop(action_list, reward, r_move_loop)
+        reward = check_power_up(new_X, new_Y, current_state, reward, r_powerup)
+        reward = check_in_flame(current_state, new_state, reward, r_lose, X, Y, new_X, new_Y)
+        reward = check_and_away_from_bomb(current_state, X, Y, new_X, new_Y, reward, r_get_away_from_bomb,
+                                          r_get_close_to_bomb, action_list)
+        return reward
+    """
+        Collect rewards for action 5
+        Check_in_flame: Checks whether the action causes agent death
+        Check_and_away_from_bomb: Checks whether the action is agent away from the bomb
+        Check_bomb_reward: checks if the action blows up wood, or if an enemy is within the blast range.
+    """
+
+    if action == 5:
+
+        reward = check_in_flame(current_state, new_state, reward, r_lose, X, Y, new_X, new_Y)
+        reward = check_and_away_from_bomb(current_state, X, Y, new_X, new_Y, reward, r_get_away_from_bomb,
+                                          r_get_close_to_bomb, action_list)
+        if current_state["ammo"] != 0:
+            reward += r_lay_bomb
+            reward = check_bomb_reward(current_state, X, Y, reward, r_wood, r_lay_bomb_near_enemy, r_attack_teammate,
+                                       enemies, teammate)
+        else:
+            # When ammo == 0, continue to lay bomb is considered as meanless action.
+            # The penalty is twice the reward for hitting the wall
+            reward += (2 * r_move_towards_wood)
+
+        return reward
+
+
+def check_bomb_reward(current_state, X, Y, reward, r_wood, r_lay_bomb_near_enemy, r_attack_teammate, enemies, teammate):
+    blast_strength = current_state["blast_strength"]
+    # Determine if there's a wall to the left of the bomb
+    for strength in range(1, blast_strength):
+        # Check if agent are outside the map boundaries
+        if Y - strength < 0:
+            break
+        # Check for wood
+        elif current_state["board"][X][Y - strength] == 2:
+            reward += r_wood
+            break
+        # If it is rigid, break
+        elif current_state["board"][X][Y - strength] == 1:
+            break
+        # If an enemy is within blast range, earn a reward
+        elif current_state["board"][X][Y - strength] in enemies:
+            reward += r_lay_bomb_near_enemy
+        elif current_state["board"][X][Y - strength] in teammate:
+            reward += r_attack_teammate
+
+    # Determine if there's a wall to the right of the bomb
+    for strength in range(1, blast_strength):
+        # Check if agent are outside the map boundaries
+        if Y + strength > 10:
+            break
+        # Check for wood
+        elif current_state["board"][X][Y + strength] == 2:
+            reward += r_wood
+            break
+        # If it is rigid, break
+        elif current_state["board"][X][Y + strength] == 1:
+            break
+        # If an enemy is within blast range, earn a reward
+        elif current_state["board"][X][Y + strength] in enemies:
+            reward += r_lay_bomb_near_enemy
+        elif current_state["board"][X][Y + strength] in teammate:
+            reward += r_attack_teammate
+
+    # Determine if there's a wall above the bomb
+    for strength in range(1, blast_strength):
+        # Check if agent are outside the map boundaries
+        if X - strength < 0:
+            break
+        # Check for wood
+        elif current_state["board"][X - strength][Y] == 2:
+            reward += r_wood
+            break
+        # If it is rigid, break
+        elif current_state["board"][X - strength][Y] == 1:
+            break
+        # If an enemy is within blast range, earn a reward
+        elif current_state["board"][X - strength][Y] in enemies:
+            reward += r_lay_bomb_near_enemy
+        elif current_state["board"][X - strength][Y] in teammate:
+            reward += r_attack_teammate
+
+    # Determine if there is a wall under the bomb
+    for strength in range(1, blast_strength):
+        # Check if agent are outside the map boundaries
+        if X + strength > 10:
+            break
+        # Check for wood
+        elif current_state["board"][X + strength][Y] == 2:
+            reward += r_wood
+            break
+        # If it is rigid, break
+        elif current_state["board"][X + strength][Y] == 1:
+            break
+        # If an enemy is within the blast range, you will receive a reward
+        elif current_state["board"][X + strength][Y] in enemies:
+            reward += r_lay_bomb_near_enemy
+        elif current_state["board"][X + strength][Y] in teammate:
+            reward += r_attack_teammate
+    return reward
+
+
+def check_in_flame(current_state, new_state, reward, r_lose, X, Y, new_X, new_Y):
+    """If the agent overlaps with the flame, it dies and returns reward"""
+
+    if current_state["flame_life"][X][Y] == 0 and new_state["flame_life"][new_X][new_Y] != 0:
+        reward += r_lose
+    return reward
+
+
+def check_power_up(new_X, new_Y, current_state, reward, r_power_up):
+    """Check whether agent eats power Up"""
+    if current_state["board"][new_X][new_Y] in [6, 7, 8]:
+        reward += r_power_up
+
+    return reward
+
+
+def check_corner_bomb(current_state, X, Y, reward, r_avoid, r_stay, current_grids):
+    """Use Action 0 to avoid bombs"""
+    # action 0 to avoid upper left bomb
+    find_bomb = False
+    if X - 1 >= 0 and Y - 1 >= 0 and current_state["board"][X - 1][Y - 1] == 3:
+        reward += r_avoid
+        find_bomb = True
+    if X - 1 >= 0 and Y - 2 >= 0 and current_state["board"][X - 1][Y - 2] == 3:
+        reward += r_avoid
+        find_bomb = True
+    # upper right
+    if X - 1 >= 0 and Y + 1 <= 10 and current_state["board"][X - 1][Y + 1] == 3:
+        reward += r_avoid
+        find_bomb = True
+    if X - 1 >= 0 and Y + 2 <= 10 and current_state["board"][X - 1][Y + 2] == 3:
+        reward += r_avoid
+        find_bomb = True
+    # lower left
+    if X + 1 <= 10 and Y - 1 >= 0 and current_state["board"][X + 1][Y - 1] == 3:
+        reward += r_avoid
+        find_bomb = True
+    if X + 2 <= 10 and Y - 1 >= 0 and current_state["board"][X + 2][Y - 1] == 3:
+        reward += r_avoid
+        find_bomb = True
+    # lower right
+    if X + 1 <= 10 and Y + 1 <= 10 and current_state["board"][X + 1][Y + 1] == 3:
+        reward += r_avoid
+        find_bomb = True
+    if X + 1 <= 10 and Y + 2 <= 10 and current_state["board"][X + 1][Y + 2] == 3:
+        reward += r_avoid
+        find_bomb = True
+    if not find_bomb and 3 not in current_grids:
+        reward += r_stay
+
+    return reward
+
+
+def check_and_away_from_bomb(current_state, X, Y, new_X, new_Y, reward, r_get_away_from_bomb, r_get_close_to_bomb,
+                             action_list):
+    """Get a reward when out of danger"""
+    # Get a reward when out of danger
+    if action_list[2] == 5 and (X != new_X or Y != new_Y):
+        reward += r_get_away_from_bomb
+    elif action_list[2] == 5 and (X == new_X and Y == new_Y):
+        reward += 2 * r_get_close_to_bomb
+    # above
+    if X - 1 >= 0 and current_state["board"][X - 1][Y] == 3 and (abs((X - 1) - new_X) + abs(Y - new_Y)) > 1:
+        reward += r_get_away_from_bomb
+    if X - 1 >= 0 and current_state["board"][X - 1][Y] == 3 and (abs((X - 1) - new_X) + abs(Y - new_Y)) == 1:
+        reward += 2 * r_get_close_to_bomb
+    if X - 2 >= 0 and current_state["board"][X - 2][Y] == 3 and (abs((X - 2) - new_X) + abs(Y - new_Y)) > 2 and \
+            current_state["board"][X - 1][Y] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif X - 2 >= 0 and current_state["board"][X - 2][Y] == 3 and (abs((X - 2) - new_X) + abs(Y - new_Y)) <= 2 and \
+            current_state["board"][X - 1][Y] not in [1, 2]:
+        reward += r_get_close_to_bomb
+    if X - 3 >= 0 and current_state["board"][X - 3][Y] == 3 and (abs((X - 3) - new_X) + abs(Y - new_Y)) > 3 and \
+            current_state["board"][X - 1][Y] not in [1, 2] and current_state["board"][X - 2][Y] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif X - 3 >= 0 and current_state["board"][X - 3][Y] == 3 and (abs((X - 3) - new_X) + abs(Y - new_Y)) <= 3 and \
+            current_state["board"][X - 1][Y] not in [1, 2] and current_state["board"][X - 2][Y] not in [1, 2]:
+        reward += r_get_close_to_bomb
+    # below
+    if X + 1 <= 10 and current_state["board"][X + 1][Y] == 3 and (abs((X + 1) - new_X) + abs(Y - new_Y)) > 1:
+        reward += r_get_away_from_bomb
+    if X + 1 <= 10 and current_state["board"][X + 1][Y] == 3 and (abs((X + 1) - new_X) + abs(Y - new_Y)) == 1:
+        reward += 2 * r_get_close_to_bomb
+    if X + 2 <= 10 and current_state["board"][X + 2][Y] == 3 and (abs((X + 2) - new_X) + abs(Y - new_Y)) > 2 and \
+            current_state["board"][X + 1][Y] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif X + 2 <= 10 and current_state["board"][X + 2][Y] == 3 and (abs((X + 2) - new_X) + abs(Y - new_Y)) <= 2 and \
+            current_state["board"][X + 1][Y] not in [1, 2]:
+        reward += r_get_close_to_bomb
+    if X + 3 <= 10 and current_state["board"][X + 3][Y] == 3 and (abs((X + 3) - new_X) + abs(Y - new_Y)) > 3 and \
+            current_state["board"][X + 1][Y] not in [1, 2] and current_state["board"][X + 2][Y] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif X + 3 <= 10 and current_state["board"][X + 3][Y] == 3 and (abs((X + 3) - new_X) + abs(Y - new_Y)) <= 3 and \
+            current_state["board"][X + 1][Y] not in [1, 2] and current_state["board"][X + 2][Y] not in [1, 2]:
+        reward += r_get_close_to_bomb
+
+    # left
+    if Y - 1 >= 0 and current_state["board"][X][Y - 1] == 3 and (abs(X - new_X) + abs((Y - 1) - new_Y)) > 1:
+        reward += r_get_away_from_bomb
+    if Y - 1 >= 0 and current_state["board"][X][Y - 1] == 3 and (abs(X - new_X) + abs((Y - 1) - new_Y)) == 1:
+        reward += 2 * r_get_close_to_bomb
+    if Y - 2 >= 0 and current_state["board"][X][Y - 2] == 3 and (abs(X - new_X) + abs((Y - 2) - new_Y)) > 2 and \
+            current_state["board"][X][Y - 1] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif Y - 2 >= 0 and current_state["board"][X][Y - 2] == 3 and (abs(X - new_X) + abs((Y - 2) - new_Y)) <= 2 and \
+            current_state["board"][X][Y - 1] not in [1, 2]:
+        reward += r_get_close_to_bomb
+    if Y - 3 >= 0 and current_state["board"][X][Y - 3] == 3 and (abs(X - new_X) + abs((Y - 3) - new_Y)) > 3 and \
+            current_state["board"][X][Y - 1] not in [1, 2] and current_state["board"][X][Y - 2] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif Y - 3 >= 0 and current_state["board"][X][Y - 3] == 3 and (abs(X - new_X) + abs((Y - 3) - new_Y)) <= 3 and \
+            current_state["board"][X][Y - 1] not in [1, 2] and current_state["board"][X][Y - 2] not in [1, 2]:
+        reward += r_get_close_to_bomb
+
+    # right
+    if Y + 1 <= 10 and current_state["board"][X][Y + 1] == 3 and (abs(X - new_X) + abs((Y + 1) - new_Y)) > 1:
+        reward += r_get_away_from_bomb
+    if Y + 1 <= 10 and current_state["board"][X][Y + 1] == 3 and (abs(X - new_X) + abs((Y + 1) - new_Y)) == 1:
+        reward += 2 * r_get_close_to_bomb
+    if Y + 2 <= 10 and current_state["board"][X][Y + 2] == 3 and (abs(X - new_X) + abs((Y + 2) - new_Y)) > 2 and \
+            current_state["board"][X][Y + 1] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif Y + 2 <= 10 and current_state["board"][X][Y + 2] == 3 and (abs(X - new_X) + abs((Y + 2) - new_Y)) <= 2 and \
+            current_state["board"][X][Y + 1] not in [1, 2]:
+        reward += r_get_close_to_bomb
+    if Y + 3 <= 10 and current_state["board"][X][Y + 3] == 3 and (abs(X - new_X) + abs((Y + 3) - new_Y)) > 3 and \
+            current_state["board"][X][Y + 1] not in [1, 2] and current_state["board"][X][Y + 2] not in [1, 2]:
+        reward += r_get_away_from_bomb
+    elif Y + 3 <= 10 and current_state["board"][X][Y + 3] == 3 and (abs(X - new_X) + abs((Y + 3) - new_Y)) <= 3 and \
+            current_state["board"][X][Y + 1] not in [1, 2] and current_state["board"][X][Y + 2] not in [1, 2]:
+        reward += r_get_close_to_bomb
+
+    # Check the corners upper left
+    if X - 1 >= 0 and Y - 1 >= 0 and current_state["board"][X - 1][Y - 1] == 3 and (abs((X - 1) - new_X)) + abs(
+            (Y - 1) - new_Y) > 2:
+        reward += r_get_away_from_bomb
+    elif X - 1 >= 0 and Y - 1 >= 0 and current_state["board"][X - 1][Y - 1] == 3 and (abs((X - 1) - new_X)) + abs(
+            (Y - 1) - new_Y) < 2:
+        reward += r_get_close_to_bomb
+    # The lower left
+    if X + 1 <= 10 and Y - 1 >= 0 and current_state["board"][X + 1][Y - 1] == 3 and (abs((X + 1) - new_X)) + abs(
+            (Y - 1) - new_Y) > 2:
+        reward += r_get_away_from_bomb
+    elif X + 1 <= 10 and Y - 1 >= 0 and current_state["board"][X + 1][Y - 1] == 3 and (abs((X + 1) - new_X)) + abs(
+            (Y - 1) - new_Y) < 2:
+        reward += r_get_close_to_bomb
+    # The upper right
+    if X - 1 >= 0 and Y + 1 <= 10 and current_state["board"][X - 1][Y + 1] == 3 and (abs((X - 1) - new_X)) + abs(
+            (Y + 1) - new_Y) > 2:
+        reward += r_get_away_from_bomb
+    elif X - 1 >= 0 and Y + 1 <= 10 and current_state["board"][X - 1][Y + 1] == 3 and (abs((X - 1) - new_X)) + abs(
+            (Y + 1) - new_Y) < 2:
+        reward += r_get_close_to_bomb
+
+    # The lower right
+    if X + 1 <= 10 and Y + 1 <= 10 and current_state["board"][X + 1][Y + 1] == 3 and (abs((X + 1) - new_X)) + abs(
+            (Y + 1) - new_Y) > 2:
+        reward += r_get_away_from_bomb
+    elif X + 1 <= 10 and Y + 1 <= 10 and current_state["board"][X + 1][Y + 1] == 3 and (abs((X + 1) - new_X)) + abs(
+            (Y - 1) + new_Y) < 2:
+        reward += r_get_close_to_bomb
+
+    return reward
+
+
+def check_move_loop(action_list, reward, r_move_loop):
+    """Penalty for performing circular meaningless actions"""
+    check_list = [[1, 2, 1, 2],
+                  [2, 1, 2, 1],
+                  [3, 4, 3, 4],
+                  [4, 3, 4, 3],
+                  [1, 2, 3, 4],
+                  [2, 1, 3, 4],
+                  [3, 4, 1, 2],
+                  [3, 4, 2, 1],
+                  [1, 2, 4, 3],
+                  [2, 1, 4, 3],
+                  [4, 3, 1, 2],
+                  [4, 3, 2, 1]]
+    if action_list in check_list:
+        reward += r_move_loop
+    return reward
+
+
+def check_dead_end(new_state, new_X, new_Y, action_list, reward, r_dead_end):
+    """Check if it's in a dead end"""
+    if action_list[2] == 5 and action_list[3] == 1:
+        if (new_Y - 1 < 0 or new_state["board"][new_X][new_Y - 1] in [1, 2, 3]) and \
+                (new_Y + 1 > 10 or new_state["board"][new_X][new_Y + 1] in [1, 2, 3]) and \
+                (new_X - 1 < 0 or new_state["board"][new_X - 1][new_Y] in [1, 2, 3]):
+            reward += r_dead_end
+            return reward
+    elif action_list[2] == 5 and action_list[3] == 2:
+        if (new_Y - 1 < 0 or new_state["board"][new_X][new_Y - 1] in [1, 2, 3]) and \
+                (new_Y + 1 > 10 or new_state["board"][new_X][new_Y + 1] in [1, 2, 3]) and \
+                (new_X + 1 > 10 or new_state["board"][new_X + 1][new_Y] in [1, 2, 3]):
+            reward += r_dead_end
+            return reward
+    elif action_list[2] == 5 and action_list[3] == 3:
+        if (new_X - 1 < 0 or new_state["board"][new_X - 1][new_Y] in [1, 2, 3]) and \
+                (new_Y - 1 < 0 or new_state["board"][new_X][new_Y - 1] in [1, 2, 3]) and \
+                (new_X + 1 > 10 or new_state["board"][new_X + 1][new_Y] in [1, 2, 3]):
+            reward += r_dead_end
+            return reward
+    elif action_list[2] == 5 and action_list[3] == 4:
+        if (new_X - 1 < 0 or new_state["board"][new_X - 1][new_Y] in [1, 2, 3]) and \
+                (new_Y + 1 > 10 or new_state["board"][new_X][new_Y + 1] in [1, 2, 3]) and \
+                (new_X + 1 > 10 or new_state["board"][new_X + 1][new_Y] in [1, 2, 3]):
+            reward += r_dead_end
+            return reward
+
+    return reward
+
+
+def check_avoid_flame(reward, r_avoid, current_grids):
+    """Check if action is used to avoid flames"""
+    if 4 in current_grids and all((grid in [1, 2, 3, 4]) for grid in current_grids):
+        reward += r_avoid
+    return reward
+
+
+def check_ignore_powerup(current_state, new_X, new_Y, action_list, current_grids, reward, r_ignore_penalty):
+    """Check whether powerUp is ignored"""
+    if ((6 or 7 or 8) in current_grids) and current_state["board"][new_X][new_Y] not in [6, 7, 8] and (
+            5 not in action_list) and \
+            current_state["ammo"] != 0:
+        reward += r_ignore_penalty
+
+    return reward
+
+
+def featurize2D(states, partially_obs=True):
+    """Process Oberservation to match network format"""
+    # There are 18 matrices
+    X = states["position"][0]
+    Y = states["position"][1]
+    shape = (11, 11)
+
+    # Process path, rigid, wood, bomb, flame, fog, power_up, agent1, agent2, agent3, agent4
+    def get_partially_obs(states, X, Y):
+        """Limit field of view in FFA environment"""
+        board = np.full(shape, 5)
+        for x in range(10):
+            for y in range(10):
+                if X - 4 <= x <= X + 4 and Y - 4 <= y <= Y + 4:
+                    board[x][y] = states["board"][x][y]
+        states["board"] = board
+        return states
+
+    def get_matrix(board, key):
+        res = board[key]
+        return res.reshape(shape).astype(np.float64)
+
+    def get_map(board, item):
+        map = np.zeros(shape)
+        map[board == item] = 1
+        return map
+
+    if partially_obs:
+        states = get_partially_obs(states, X, Y)
+
+    board = get_matrix(states, "board")
+
+    path = get_map(board, 0)
+    rigid = get_map(board, 1)
+    wood = get_map(board, 2)
+    bomb = get_map(board, 3)
+    flame = get_map(board, 4)
+    # fog = get_map(board, 5)
+    fog = np.zeros(shape)
+    agent1 = get_map(board, 10)
+    agent2 = get_map(board, 11)
+    agent3 = get_map(board, 12)
+    agent4 = get_map(board, 13)
+
+    power_up = []
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 6 or num == 7 or num == 8:
+                new_row.append(1)
+            else:
+                new_row.append(0.0)
+        power_up.append(new_row)
+
+    bomb_blast_strength = get_matrix(states, 'bomb_blast_strength')
+    bomb_life = get_matrix(states, 'bomb_life')
+    bomb_moving_direction = get_matrix(states, 'bomb_moving_direction')
+    flame_life = get_matrix(states, 'flame_life')
+
+    ammo_2D, blast_strength_2D, can_kick_2D = rebuild_1D_element(states)
+
+    feature2D = [path, rigid, wood, bomb, flame, fog, power_up, agent1, agent2, agent3, agent4, bomb_blast_strength,
+                 bomb_life, bomb_moving_direction, flame_life, ammo_2D, blast_strength_2D, can_kick_2D]
+
+    return np.array(feature2D)
+
+
+def rebuild_1D_element(states):
+    """Process some 1D data in Oberservation"""
+    shape = (11, 11)
+
+    ammo = states["ammo"]
+    ammo_2D = np.full(shape, ammo)
+
+    blast_strength = states["blast_strength"]
+    blast_strength_2D = np.full(shape, blast_strength)
+
+    can_kick = states["can_kick"]
+    can_kick_2D = np.full(shape, int(can_kick))
+
+    return ammo_2D, blast_strength_2D, can_kick_2D
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..528dc3c
--- /dev/null
+++ b/README.md
@@ -0,0 +1,71 @@
+# Architecture
+
+We were able to implement all the descendants of Rainbow DQN except Categorical DQN
+
+- [x] basic DQN
+- [x] Double DQN
+- [x] Prioritized Experience Replay
+- [x] Dueling Network Architectures
+- [x] Noisy Nets
+- [x] Multi Step Reinforcement Learning
+- [ ] Categorical DQN
+
+# Getting Started with our DQNAgent
+
+# Pre-requisites
+
+* [Python 3.6.0](https://www.python.org/downloads/release/python-360/)+ (including `pip`)
+* [Docker](https://www.docker.com/) (only needed for `DockerAgent`)
+* [tensorflow 2.6.2](https://www.tensorflow.org/hub/installation)
+* [Keras 2.6.0](https://keras.io/getting_started/)
+* Others are all included in [requirements](Group_C/requirements.txt)
+# Installation
+
+* Clone the repository
+```
+$ git clone https://github.com/oxFFFF-Q/Project_AI.git
+```
+
+## Pip
+
+* Install the `pommerman` package. This needs to be done every time the code is updated to get the
+latest modules
+```
+$ cd ~/playground
+$ pip install -U .
+```
+
+## Conda
+
+* Install the `pommerman` environment.
+```
+$ cd ~/playground
+$ conda env create -f env.yml
+$ conda activate pommerman
+```
+
+* To update the environment
+```
+$ conda env update -f env.yml --prune
+```
+
+# Launch the agent
+We have seperately trained models for player 1 [Agent1](Group_C/agents/Agent1.py) and player 3 [Agent3](Group_C/agents/Agent3.py). Run [main_test.py](Group_C/main_test.py) to test them palying against two [SimpleAgent](pommerman/agents/simple_agent.py).
+
+# Train your agent
+
+## A Simple Example
+
+Run [main_train.py](Group_C/main_train.py) to train our final DQN model for radio team competition of two [SimpleAgent](pommerman/agents/simple_agent.py) as enemies and a [SimpleAgent](pommerman/agents/simple_agent.py) as teammate.
+
+The training will not automatically stop, but need to be done manully, according to the given out report about the rewards. The paramaters will be recorded every 100 episodes. Run [main_save_model.py](Group_C/main_save_model.py) to save the model. The name of the model is required. The best one is usually among the last few models.
+
+## Use other strategies
+
+Select other names for `strategy` in [main_train.py](Group_C/main_train.py) to try other achietectures. Make sure of the consistency of the `strategy` in [main_save_model.py](Group_C/main_save_model.py).
+
+
+
+# Visualize the experiment results
+
+Our experiment results are all stored in [data](Group_C/result_image/data). Run [make_image.py](Group_C/result_image/make_image.py) to get a visualization of them.
diff --git a/docs/README.md b/docs/README.md
index 126cd78..528dc3c 100755
--- a/docs/README.md
+++ b/docs/README.md
@@ -1,30 +1,33 @@
-# Getting Started
+# Architecture
+
+We were able to implement all the descendants of Rainbow DQN except Categorical DQN
+
+- [x] basic DQN
+- [x] Double DQN
+- [x] Prioritized Experience Replay
+- [x] Dueling Network Architectures
+- [x] Noisy Nets
+- [x] Multi Step Reinforcement Learning
+- [ ] Categorical DQN
+
+# Getting Started with our DQNAgent
 
 # Pre-requisites
 
 * [Python 3.6.0](https://www.python.org/downloads/release/python-360/)+ (including `pip`)
 * [Docker](https://www.docker.com/) (only needed for `DockerAgent`)
-* [virtualenv](https://virtualenv.pypa.io/en/stable/) (optional, for isolated Python environment)
-
+* [tensorflow 2.6.2](https://www.tensorflow.org/hub/installation)
+* [Keras 2.6.0](https://keras.io/getting_started/)
+* Others are all included in [requirements](Group_C/requirements.txt)
 # Installation
 
 * Clone the repository
 ```
-$ git clone https://github.com/MultiAgentLearning/playground ~/playground
+$ git clone https://github.com/oxFFFF-Q/Project_AI.git
 ```
 
 ## Pip
 
-* **OPTIONAL**: Setup an isolated virtual Python environment by running the following commands
-```
-$ virtualenv ~/venv
-```
-This environment needs to be activated for usage. Any package installations will now persist
-in this virtual environment folder only.
-```
-source ~/venv/bin/activate
-```
-
 * Install the `pommerman` package. This needs to be done every time the code is updated to get the
 latest modules
 ```
@@ -46,83 +49,23 @@ $ conda activate pommerman
 $ conda env update -f env.yml --prune
 ```
 
-# Examples
-
-## A Simple Example
-
-The [simple_ffa_run.py](../examples/simple_ffa_run.py) runs a sample Free-For-All game with two
-[SimpleAgent](../pommerman/agents/simple_agent.py)s and two [RandomAgent](../pommerman/agents/random_agent.py)s
-on the board.
-
-## Using A Docker Agent
-
-The above example can be extended to use [DockerAgent](../pommerman/agents/docker_agent.py) instead of a
-[RandomAgent](../pommerman/agents/random_agent.py). [examples/docker-agent](../examples/docker-agent) contains
-the code to wrap a [SimpleAgent](../pommerman/agents/simple_agent.py) inside Docker.
-
-
-* We will build a docker image with the name "pommerman/simple-agent" using the `Dockerfile` provided.
-```
-$ cd ~/playground
-$ docker build -t pommerman/simple-agent -f examples/docker-agent/Dockerfile .
-```
-
-* The agent list seen in the previous example can now be updated. Note that a `port` argument (of an unoccupied port) is
-needed to expose the HTTP server.
-```python
-agent_list = [
-    agents.SimpleAgent(),
-    agents.RandomAgent(),
-    agents.SimpleAgent(),
-    agents.DockerAgent("pommerman/simple-agent", port=12345)
-]
-```
-
-## Playing an interactive game
-
-You can also play the game! See below for an example where one [PlayerAgent](../pommerman/agents/player_agent.py)
-controls with the `arrow` keys and the other with the `wasd` keys.
+# Launch the agent
+We have seperately trained models for player 1 [Agent1](Group_C/agents/Agent1.py) and player 3 [Agent3](Group_C/agents/Agent3.py). Run [main_test.py](Group_C/main_test.py) to test them palying against two [SimpleAgent](pommerman/agents/simple_agent.py).
 
+# Train your agent
 
-```python
-agent_list = [
-    agents.SimpleAgent(),
-    agents.PlayerAgent(agent_control="arrows"), # arrows to move, space to lay bomb
-    agents.SimpleAgent(),
-    agents.PlayerAgent(agent_control="wasd"), # W,A,S,D to move, E to lay bomb
-]
-```
-
-## Submitting an Agent.
-
-In order to submit an agent, you need to create an account at 
-[pommerman.com](https://pommerman.com). You can do this by registering with your 
-email address or logging in with your Github account.
-
-Once you have created an account, login and navigate to your profile - 
-[Pommerman profile](https://pommerman.com/me). To submit an agent, fill in the 
-form with your agent's name, an ssh git url, and the path to your agent's Docker 
-file from the github repository's top level directory. Please make sure that 
-your docker file builds properly beforehand.
+## A Simple Example
 
-Next, you will need to add an ssh deploy key to your account so we can access 
-your agent's repo. This is provided to you along with instructions after 
-registering the agent.
+Run [main_train.py](Group_C/main_train.py) to train our final DQN model for radio team competition of two [SimpleAgent](pommerman/agents/simple_agent.py) as enemies and a [SimpleAgent](pommerman/agents/simple_agent.py) as teammate.
 
-Before doing all of this, note that we use Docker to run the agents. The best example for making a Docker agent is in the repo in the examples/docker-agent directory. This *must* work in order to properly enter an agent, and we suggest using the accompanying pom_battle cli command (or equivalently run_battle.py) to test out your Docker implementation. If you are having trouble still, feel free to ask questions on our Discord channel.
+The training will not automatically stop, but need to be done manully, according to the given out report about the rewards. The paramaters will be recorded every 100 episodes. Run [main_save_model.py](Group_C/main_save_model.py) to save the model. The name of the model is required. The best one is usually among the last few models.
 
-## NIPS Competition Information:
+## Use other strategies
 
-Each competitor will submit two agents that will be teamed together. These agents can be the same one and can be in the same repository even, but we expect there to be two submissions for each entrant. We additionally expect there to be notable differences among the submissions. Similarly to the June 3rd competition, we will examine the code before running it on our servers and collusion will not be tolerated.
+Select other names for `strategy` in [main_train.py](Group_C/main_train.py) to try other achietectures. Make sure of the consistency of the `strategy` in [main_save_model.py](Group_C/main_save_model.py).
 
-The competition will be held live at NIPS 2018 in Montreal. We would prefer it if serious entrants were there, but that is not a requirement.
 
-## Actually Getting Started
 
-Here is some information that may help you more quickly develop successful agents:
+# Visualize the experiment results
 
-1. Two agents cannot move to the same cell. They will bounce back to their prior places if they try. The same applies to bombs. If an agent and a bomb both try to move to the same space, then the agent will succeed but the bomb will bounce back.
-2. If an agent with the can_kick ability moves to a cell with a bomb, then the bomb is kicked in the direction from which the agent came. The ensuing motion will persist until the bomb hits a wall, another agent, or the edge of the grid. 
-3. When a bomb explodes, it immediately reaches its full blast radius. If there is an agent or a wall in the way, then it prematurely ends and destroys that agent or wall. 
-4. If a bomb is in the vicinity of an explosion, then it will also go off. In this way, bombs can chain together.
-5. The SimpleAgent is very useful as a barometer for your own efforts. Four SimpleAgents playing against each other have a win rate of ~18% each with the remaining ~28% of the time being a tie. Keep in mind that it _can_ destroy itself. That can skew your own results if not properly understood.
+Our experiment results are all stored in [data](Group_C/result_image/data). Run [make_image.py](Group_C/result_image/make_image.py) to get a visualization of them.
diff --git a/examples/simple_ffa_run.py b/examples/simple_ffa_run.py
index b364a1f..239d7d9 100755
--- a/examples/simple_ffa_run.py
+++ b/examples/simple_ffa_run.py
@@ -1,16 +1,21 @@
 '''An example to show how to set up an pommerman game programmatically'''
 import pommerman
+import random
+import numpy as np
+import tensorflow as tf
 from pommerman import agents
 
 
 def main():
     '''Simple function to bootstrap a game.
-       
+
        Use this as an example to set up your training env.
     '''
     # Print all possible environments in the Pommerman registry
     print(pommerman.REGISTRY)
-
+    random.seed(1)
+    #np.random.seed(1)
+    #tf.random.set_seed(1)
     # Create a set of agents (exactly four)
     agent_list = [
         agents.SimpleAgent(),
diff --git a/pommerman/__init__.py b/pommerman/__init__.py
index 7c6ea33..fb7ed75 100755
--- a/pommerman/__init__.py
+++ b/pommerman/__init__.py
@@ -7,7 +7,7 @@
 from . import forward_model
 from . import helpers
 from . import utility
-from . import network
+#from . import network
 
 gym.logger.set_level(40)
 REGISTRY = None
diff --git a/pommerman/constants.py b/pommerman/constants.py
index 27b1ef4..4ca8570 100755
--- a/pommerman/constants.py
+++ b/pommerman/constants.py
@@ -26,7 +26,7 @@
                                                                    117)]
 # If using collapsing boards, the step at which the board starts to collapse.
 FIRST_COLLAPSE = 500
-MAX_STEPS = 800
+MAX_STEPS = 300
 RADIO_VOCAB_SIZE = 8
 RADIO_NUM_WORDS = 2
 
diff --git a/pommerman/envs/v2.py b/pommerman/envs/v2.py
index a2a3985..947a56a 100755
--- a/pommerman/envs/v2.py
+++ b/pommerman/envs/v2.py
@@ -79,6 +79,7 @@ def get_observations(self):
         self.observations = observations
         return observations
 
+    # TODO radio
     def step(self, actions):
         personal_actions = []
         radio_actions = []
diff --git a/pommerman/graphics.py b/pommerman/graphics.py
index 18e57d0..874c45a 100755
--- a/pommerman/graphics.py
+++ b/pommerman/graphics.py
@@ -9,17 +9,18 @@
 from random import randint
 from time import strftime
 
-from gym.utils import reraise
+#from gym.utils import reraise
 import numpy as np
+import pyglet
 from PIL import Image
 
-try:
-    import pyglet
-except ImportError as error:
-    reraise(
-        suffix="Install pyglet with 'pip install pyglet'. If you want to just "
-        "install all Gym dependencies, run 'pip install -e .[all]' or "
-        "'pip install gym[all]'.")
+# try:
+#
+# except ImportError as error:
+#     reraise(
+#         suffix="Install pyglet with 'pip install pyglet'. If you want to just "
+#         "install all Gym dependencies, run 'pip install -e .[all]' or "
+#         "'pip install gym[all]'.")
 
 try:
     from pyglet.gl import *
diff --git a/test/DQN2Agent.py b/test/DQN2Agent.py
index 7507e47..953ac56 100644
--- a/test/DQN2Agent.py
+++ b/test/DQN2Agent.py
@@ -1,4 +1,5 @@
 from pommerman.agents.simple_agent import SimpleAgent
+from pommerman.agents.random_agent import RandomAgent
 import torch
 import torch.nn as nn
 import torch.optim as optim
@@ -9,6 +10,7 @@
 import gym
 import numpy as np
 from utils import featurize, featurize2
+import os
 
 from pommerman.agents import BaseAgent
 from replay_buffer import ReplayBuffer, ReplayBuffer2
@@ -22,6 +24,7 @@ def __init__(self, env, args, character=characters.Bomber):
         super(DQN2Agent, self).__init__(character)
         self.obs_n = env.observation_space.shape[0]  # output_dim
         self.action_n = 6  # input_dim
+        self.action_space = [0,1,2,3,4,5]
         self.env = env
 
         self.epsilon = args.epsilon
@@ -59,38 +62,220 @@ def forward(self, state):
         return qvals
 
     def act(self, obs, action_space):
-        return self.baseAgent.act(obs,self.action_n)
+        return random.randrange(0,6,1)
+        #return self.baseAgent.act(obs,self.action_n)
 
     def dqnact(self, obs):
-        action = self.eval_net.forward(obs)[0]
+        #action = self.eval_net.forward(obs)[0]
+        #result = (torch.max(action, 0)[1]).numpy()
+        lx = obs['local']
+        ax = obs['additional']
+        action = self.eval_net.forward1(lx,ax)[0]
         result = (torch.max(action, 0)[1]).numpy()
         return result
 
+    def reward(self, featurel, featurea, action, sl, sa, rewards):
+        # set up reward
+        r_wood = 0.05
+        r_powerup = 0.2
+        r_put_bomb = 0.08
+        r_win = 0.5
+        r_fail = -1
+        r_kick = 0.2
+        r_hit = -0.2
+        r_stay_in_danger = -0.2
+        r_escape = 0.2
+
+        rigid = featurel[0].numpy()
+        wood = featurel[1].numpy()
+        bomb = featurel[2].numpy()
+        power_up = featurel[3]
+        fog = featurel[4]
+        agent1 = featurel[5].numpy()
+        sagent1 = sl[5].numpy()
+        agent2 = featurel[6].numpy()
+        agent3 = featurel[7].numpy()
+        agent4 = featurel[8].numpy()
+        flame = featurel[9]
+        position0 = int(featurea[0].item())
+        position1 = int(featurea[1].item())
+        p0 = int(sa[0].item())
+        p1 = int(sa[1].item())
+        ammo = int(sa[2].item())
+        blast_strength = int(featurea[3].item())
+        can_kick = int(featurea[4].item())
+        teammate = int(featurea[5].item())
+        enemies = int(featurea[6].item())
+        message = int(featurea[7].item())
+        rewards = rewards.numpy()
+        reward = 0
+        #sagents = sl[4]
+        sbomb = sl[2].numpy()
+        action = int(action.item())
+
+
+
+        # print(agent1)
+        # print(p0, p1)
+        # print(position0, position1)
+        # print('len:', len(agent1))
+        # print('shape:', agent1.shape)
+        # print('-------------------')
+        # # reward_done
+        # # print(rewards)
+
+        if rewards == 1:
+            reward += r_win
+        if rewards == -1:
+            reward += r_fail
+
+        # reward_powerup
+        # sammo = int(sa[2].item())
+        # if ammo > 1 and ammo > sammo:
+        #     reward += r_powerup
+        # sstrength = int(sa[3].item())
+        # if blast_strength > sstrength:
+        #     reward += r_powerup
+        # skick = int(sa[4].item())
+        # if can_kick and not skick:
+        #     reward += r_powerup
+
+        # reward_wood
+        if ammo > 0 and action == 5:
+            bomb_flame = self.build_flame(position0, position1, rigid, blast_strength)
+            num_wood = np.count_nonzero(wood*bomb_flame == 1)
+            reward += num_wood*r_wood
+
+        # reward_kick
+        # if sbomb[position0, position1] == 1 and rewards != -1:
+        #     reward += r_kick
+        # reward_hit_wood
+        if action>0 and action<5:
+            if (p0,p1) == (position0,position1):
+                reward += r_hit
+        
+        # reward_escape
+        bomb_list = []
+        for i in range(11):
+            for j in range(11):
+                if sbomb[i,j] == 1:
+                    bomb_list.append((i,j))
+        
+        for b in bomb_list:
+            bomb_flame1 = self.build_flame(b[0], b[1], rigid, 8)
+            if bomb_flame1[position0, position1] == 1:
+                reward += r_stay_in_danger
+                #print(bomb_flame1)
+
+            
+        
+        # reward escape from bomb
+
+        """
+        exist_bomb = []
+        for row, rowbomb in enumerate(bomb):
+            for col, _ in enumerate(rowbomb):
+                if bomb[row, col] == 1:
+                    exist_bomb.append((row, col))
+        #print(bomb)
+        #print(exist_bomb)
+        
+        if exist_bomb:
+            for ebomb in exist_bomb:
+                bomb_flame1 = self.build_flame(ebomb[0], ebomb[1], rigid, blast_strength)
+                if bomb_flame1[position0, position1] == 1:
+                    reward -= 0.5
+                #print(bomb_flame1)
+        """
+        return reward
+
+    def build_flame(self, position0, position1, rigid, blast_strength):
+        
+        position_bomb = np.array([position0,position1])
+        m = position_bomb[0]
+        n = position_bomb[1]
+        l = blast_strength
+        f = [l,l,l,l]       # Scope of flame: up down left right
+        bomb_flame = np.zeros_like(rigid)
+
+        # 判断实体墙或边界是否阻断火焰
+        flame_up = np.zeros_like(bomb_flame)
+        flame_down = np.zeros_like(bomb_flame)
+        flame_left = np.zeros_like(bomb_flame)
+        flame_right = np.zeros_like(bomb_flame)
+        if m - f[0] < 0:  # 上边界
+            f[0] = m
+        flame_up[m - f[0]:m, n] = 1
+        if m + f[1] > bomb_flame.shape[0] - 1:  # 下边界
+            f[1] = bomb_flame.shape[0] - 1 - m
+        flame_down[m + 1:m + f[1] + 1, n] = 1
+        if n - f[2] < 0:  # 左边界
+            f[2] = n
+        flame_left[m, n - f[2]:n] = 1
+        if n + f[3] > bomb_flame.shape[0] - 1:  # 右边界
+            f[3] = bomb_flame.shape[0] - 1 - n
+        flame_right[m, n + 1:n + f[3] + 1] = 1
+
+        rigid_0 = flame_up * rigid
+        rigid_1 = flame_down * rigid
+        rigid_2 = flame_left * rigid
+        rigid_3 = flame_right * rigid
+        if np.argwhere(rigid_0==1).size != 0:    # 上实体墙
+            rigid_up = np.max(np.argwhere(rigid_0==1)[:,0][0])
+            if rigid_up >= m-f[0]:
+                f[0] = m - rigid_up - 1
+        if np.argwhere(rigid_1==1).size != 0:   # 下实体墙
+            rigid_down = np.min(np.argwhere(rigid_1 == 1)[:, 0][0])
+            if rigid_down <= m+f[1]:
+                f[1] = rigid_down - m - 1
+        if np.argwhere(rigid_2==1).size != 0:  # 左实体墙
+            rigid_left = np.max(np.argwhere(rigid_2 == 1)[0, :][1])
+            if rigid_left >= n-f[2]:
+                f[2] = n - rigid_left - 1
+        if np.argwhere(rigid_3==1).size != 0:  # 右实体墙
+            rigid_right = np.min(np.argwhere(rigid_3 == 1)[0, :][1])
+            if rigid_right <= n+f[3]:
+                f[3] = rigid_right - n - 1
+        bomb_flame[m-f[0]:m+f[1]+1, n] = 1
+        bomb_flame[m, n-f[2]:n+f[3]+1] = 1
+
+        return bomb_flame
+
     def update(self, gamma, batch_size):
         if self.learn_step_counter % 10 == 0:
             self.target_net.load_state_dict(self.eval_net.state_dict())
         self.learn_step_counter += 1
 
-        states, actions, rewards, next_states, done = self.buffer.sample2(batch_size)
-        action_index = actions.squeeze(-2)[:,0].unsqueeze(1)
-        curr_Q_batch = self.eval_net(states)[:,0]
-        curr_Q = curr_Q_batch.gather(1, action_index).squeeze(-1)
-        #print(curr_Q)
+        statesl, statesa, actions, rewards, next_statesl, next_statesa, done = self.buffer.sample(batch_size)
+        #print(rewards)
+        #print(actions)
+        action_index = actions.squeeze(-2)
+        #print(action_index)
+        curr_Q_batch = self.eval_net(statesl, statesa)#[:,0]
+        curr_Q = curr_Q_batch.gather(1, action_index.type(torch.int64)).squeeze(-1)
         
-        next_batch = self.target_net(next_states)[:,0]
+        next_batch = self.target_net(next_statesl, next_statesa)#[:,0]
         next_Q = torch.max(next_batch,1)[0]
-        #print(next_Q)
+        #计算reward
+        computed_reward = []
+        for l, a, action, sl, sa, re in zip(next_statesl, next_statesa, actions, statesl, statesa, rewards):
+            computed_reward.append(self.reward(l, a, action, sl, sa, re))
+
 
-        rewards_batch = rewards.squeeze(-2)[:,0]
+        #这是得到的reward
+        computed_reward = torch.tensor(computed_reward)
+        
+        rewards_batch = computed_reward
         #print(rewards_batch)
+        
+        
         # expected_Q = rewards + self.gamma * torch.max(next_Q, 1)
-        expected_Q = gamma * next_Q + rewards_batch
-
+        expected_Q = (gamma * next_Q + rewards_batch) * ~done + done * rewards_batch
         # max_q_prime = next_Q.max(1)[0].unsqueeze(1)
         # expected_Q = done * (rewards + gamma * max_q_prime) + (1 - done) * 1 / (1 - gamma) * rewards
         # expected_Q = done * (rewards + gamma * max_q_prime) + 1 / (1 - gamma) * rewards
-        loss = self.MSE_loss(curr_Q, expected_Q) # TODO: try Huber Loss later too
-
+        loss = self.MSE_loss(curr_Q, expected_Q[0]) # TODO: try Huber Loss later too
+        # print('loss:', loss)
         self.optim.zero_grad()
         loss.backward()
         self.optim.step()
@@ -98,19 +283,58 @@ def update(self, gamma, batch_size):
     def epsdecay(self):
         self.epsilon = self.epsilon * self.eps_decay if self.epsilon > self.min_eps else self.epsilon
 
+    # def lrdecay(self,epi):
+    #     epi = self.epi
+    #     self.lr = self.lr * self.lr_decay ** (epi) if s
+
+    def save_model(self):
+        torch.save({'dqn2Net': self.eval_net.state_dict(),'optimizer2_state_dict': self.optim.state_dict()}, 'model_dqn2.pt')
+    
+    def load_model(self):
+        if os.path.exists('model_dqn2.pt'):
+            state_dict = torch.load('model_dqn2.pt')
+            self.eval_net.load_state_dict(state_dict['dqn2Net'])
+            self.optim.load_state_dict(state_dict['optimizer2_state_dict'])
+            self.target_net.load_state_dict(self.eval_net.state_dict())
 
 class Net2(nn.Module):
     def __init__(self,env):
         super(Net2,self).__init__()
-        self.obs_n = env.observation_space.shape[0]
-        self.fc1 = nn.Linear(374,128)
-        self.fc1.weight.data.normal_(0, 0.1)
-        self.out = nn.Linear(128,6)
-        self.out.weight.data.normal_(0, 0.1)
-
-    def forward(self, x):
-        x = torch.FloatTensor(x)
-        x = self.fc1(x)
-        x = F.relu(x)
-        qvals = self.out(x)
-        return qvals
\ No newline at end of file
+        self.features = nn.Sequential(
+            nn.Conv2d(14,32,2,stride=1,padding=1),
+            nn.ReLU(),
+            nn.Conv2d(32,64,3,stride=1,padding=1),
+            nn.ReLU(),
+            nn.Conv2d(64,64,3,stride=1,padding=1),
+            nn.AdaptiveAvgPool2d(1)
+        )
+        self.fc = nn.Sequential(
+            nn.Linear(75, 32),
+            nn.ReLU(),
+            nn.Linear(32, 16),
+            nn.ReLU(),
+            nn.Linear(16, 6)
+        )
+
+    def forward(self, lx, ax):
+        lx = torch.FloatTensor(lx)
+        ax = torch.FloatTensor(ax)
+        #lx = lx.unsqueeze(3)
+        #print(lx[0])
+        #x = torch.unsqueeze(x, dim=0).float()
+        lx = self.features(lx)
+        lx = lx.view(lx.size(0), -1)
+        outx = torch.cat((lx, ax), 1)
+        out = self.fc(outx)
+        return out
+
+    def forward1(self, lx, ax):
+        lx = torch.FloatTensor(lx)
+        ax = torch.FloatTensor(ax)
+        lx = lx.unsqueeze(0)
+        lx = self.features(lx)
+        lx = lx.view(lx.size(0), -1)
+        ax = ax.unsqueeze(0)
+        outx = torch.cat((lx, ax), 1)
+        out = self.fc(outx)
+        return out
\ No newline at end of file
diff --git a/test/DQNAgent.py b/test/DQNAgent.py
index 73a9f62..c7381a4 100644
--- a/test/DQNAgent.py
+++ b/test/DQNAgent.py
@@ -1,4 +1,4 @@
-from pommerman.agents.simple_agent import SimpleAgent
+from pommerman.agents.simple_agent import SimpleAgent #random_agent
 import torch
 import torch.nn as nn
 import torch.optim as optim
@@ -8,9 +8,11 @@
 import random
 import numpy as np
 from utils import featurize
+import os
 
 from pommerman.agents import BaseAgent
 from replay_buffer import ReplayBuffer, ReplayBuffer2
+from plot import plot_reward
 
 
 class DQNAgent(BaseAgent):
@@ -67,121 +69,167 @@ def dqnact(self, obs):
         result = (torch.max(action, 0)[1]).numpy()
         return result
 
-    def reward(self, featurel, featurea, action, sl, sa, epistep):
+    def reward(self, featurel, featurea, action, sl, sa, rewards):
         # set up reward
-        r_wood = 0.1
+        r_wood = 0.05
+        r_powerup = 0.2
+        r_put_bomb = 0.08
+        r_win = 0.5
+        r_fail = -1
+        r_kick = 0.2
+        r_hit = -0.2
 
         rigid = featurel[0].numpy()
         wood = featurel[1].numpy()
-        bomb = featurel[2]
-        agents = featurel[4]
+        bomb = featurel[2].numpy()
+        agents = featurel[4].numpy()
         power_up = featurel[3]
+        flame = featurel[9]
         position0 = int(featurea[0].item())
         position1 = int(featurea[1].item())
+        p0 = int(sa[0].item())
+        p1 = int(sa[1].item())
         ammo = int(featurea[2].item())
         blast_strength = int(featurea[3].item())
         can_kick = int(featurea[4].item())
         teammate = int(featurea[5].item())
         enemies = int(featurea[6].item())
+        rewards = rewards.numpy()
         reward = 0
-        sagents = sl[4]
-        sbombs = sl[2]
-        es = epistep.tolist()
-        es[1] += 2
+        #sagents = sl[4]
+        sbomb = sl[2].numpy()
+        action = int(action.item())
+
+        # reward_done
+        #print(rewards)
+        if rewards == 1:
+            reward += r_win
+        if rewards == -1:
+            reward += r_fail
+
+        # reward_powerup
+        sammo = int(sa[2].item())
+        if ammo > 1 and ammo > sammo:
+            reward += r_powerup
+        sstrength = int(sa[3].item())
+        if blast_strength > sstrength:
+            reward += r_powerup
+        skick = int(sa[4].item())
+        if can_kick and not skick:
+            reward += r_powerup
+        #print(action)
 
         # reward_wood
-        if int(action[0].item()) == 5:
-            position_bomb = np.array([position0,position1])
-            m = position_bomb[0]
-            n = position_bomb[1]
-            l = blast_strength
-            f = [l,l,l,l]       # Scope of flame: up down left right
-            bomb_flame = np.zeros_like(bomb.numpy())
-
-            # 判断实体墙或边界是否阻断火焰
-            flame_up = np.zeros_like(bomb_flame)
-            flame_down = np.zeros_like(bomb_flame)
-            flame_left = np.zeros_like(bomb_flame)
-            flame_right = np.zeros_like(bomb_flame)
-            if m - f[0] < 0:  # 上边界
-                f[0] = m
-            flame_up[m - f[0]:m, n] = 1
-            if m + f[1] > bomb_flame.shape[0] - 1:  # 下边界
-                f[1] = bomb_flame.shape[0] - 1 - m
-            flame_down[m + 1:m + f[1] + 1, n] = 1
-            if n - f[2] < 0:  # 左边界
-                f[2] = n
-            flame_left[m, n - f[2]:n] = 1
-            if n + f[3] > bomb_flame.shape[0] - 1:  # 右边界
-                f[3] = bomb_flame.shape[0] - 1 - n
-            flame_right[m, n + 1:n + f[3] + 1] = 1
-
-            rigid_0 = flame_up * rigid
-            rigid_1 = flame_down * rigid
-            rigid_2 = flame_left * rigid
-            rigid_3 = flame_right * rigid
-            if np.argwhere(rigid_0==1).size != 0:    # 上实体墙
-                rigid_up = np.max(np.argwhere(rigid_0==1)[:,0][0])
-                if rigid_up >= m-f[0]:
-                    f[0] = m - rigid_up - 1
-            if np.argwhere(rigid_1==1).size != 0:   # 下实体墙
-                rigid_down = np.min(np.argwhere(rigid_1 == 1)[:, 0][0])
-                if rigid_down <= m+f[1]:
-                    f[1] = rigid_down - m - 1
-            if np.argwhere(rigid_2==1).size != 0:  # 左实体墙
-                rigid_left = np.max(np.argwhere(rigid_2 == 1)[0, :][1])
-                if rigid_left >= n-f[2]:
-                    f[2] = n - rigid_left - 1
-            if np.argwhere(rigid_3==1).size != 0:  # 右实体墙
-                rigid_right = np.min(np.argwhere(rigid_3 == 1)[0, :][1])
-                if rigid_right <= n+f[3]:
-                    f[3] = rigid_right - n - 1
-            bomb_flame[m-f[0]:m+f[1]+1, n] = 1
-            bomb_flame[m, n-f[2]:n+f[3]+1] = 1
+        if action == 5:
+            reward += r_put_bomb
+            bomb_flame = self.build_flame(position0, position1, rigid, blast_strength)
             num_wood = np.count_nonzero(wood*bomb_flame == 1)
             reward += num_wood*r_wood
-            '''
-            # test
-            print('rigid')
-            print(rigid)
-            print('position_bomb')
-            print(position_bomb)
-            print('f')
-            print(f)
-            print('l')
-            print(l)
-            print('bomb_flame')
-            print(bomb_flame)
-            print('num_wood')
-            print(num_wood)
-            print('-------------------------------------')
-            '''
+
+        # reward_kick
+        if sbomb[position0, position1] == 1 and rewards != -1:
+            reward += r_kick
+        # reward_hit_wood
+        if action>0 or action<5:
+            if (p0,p1) == (position0,position1):
+                reward += r_hit
+        """
+        exist_bomb = []
+        for row, rowbomb in enumerate(bomb):
+            for col, _ in enumerate(rowbomb):
+                if bomb[row, col] == 1:
+                    exist_bomb.append((row, col))
+        #print(bomb)
+        #print(exist_bomb)
+        
+        if exist_bomb:
+            for ebomb in exist_bomb:
+                bomb_flame1 = self.build_flame(ebomb[0], ebomb[1], rigid, blast_strength)
+                if bomb_flame1[position0, position1] == 1:
+                    reward -= 0.5
+                #print(bomb_flame1)
+        """
+
+
         return reward
 
-    def update(self, gamma, batch_size,episode, step):
-        #每走十步学习一次
+    def build_flame(self, position0, position1, rigid, blast_strength):
+        
+        position_bomb = np.array([position0,position1])
+        m = position_bomb[0]
+        n = position_bomb[1]
+        l = blast_strength - 1
+        f = [l,l,l,l]       # Scope of flame: up down left right
+        bomb_flame = np.zeros_like(rigid)
+
+        # 判断实体墙或边界是否阻断火焰
+        flame_up = np.zeros_like(bomb_flame)
+        flame_down = np.zeros_like(bomb_flame)
+        flame_left = np.zeros_like(bomb_flame)
+        flame_right = np.zeros_like(bomb_flame)
+        if m - f[0] < 0:  # 上边界
+            f[0] = m
+        flame_up[m - f[0]:m, n] = 1
+        if m + f[1] > bomb_flame.shape[0] - 1:  # 下边界
+            f[1] = bomb_flame.shape[0] - 1 - m
+        flame_down[m + 1:m + f[1] + 1, n] = 1
+        if n - f[2] < 0:  # 左边界
+            f[2] = n
+        flame_left[m, n - f[2]:n] = 1
+        if n + f[3] > bomb_flame.shape[0] - 1:  # 右边界
+            f[3] = bomb_flame.shape[0] - 1 - n
+        flame_right[m, n + 1:n + f[3] + 1] = 1
+
+        rigid_0 = flame_up * rigid
+        rigid_1 = flame_down * rigid
+        rigid_2 = flame_left * rigid
+        rigid_3 = flame_right * rigid
+        if np.argwhere(rigid_0==1).size != 0:    # 上实体墙
+            rigid_up = np.max(np.argwhere(rigid_0==1)[:,0][0])
+            if rigid_up >= m-f[0]:
+                f[0] = m - rigid_up - 1
+        if np.argwhere(rigid_1==1).size != 0:   # 下实体墙
+            rigid_down = np.min(np.argwhere(rigid_1 == 1)[:, 0][0])
+            if rigid_down <= m+f[1]:
+                f[1] = rigid_down - m - 1
+        if np.argwhere(rigid_2==1).size != 0:  # 左实体墙
+            rigid_left = np.max(np.argwhere(rigid_2 == 1)[0, :][1])
+            if rigid_left >= n-f[2]:
+                f[2] = n - rigid_left - 1
+        if np.argwhere(rigid_3==1).size != 0:  # 右实体墙
+            rigid_right = np.min(np.argwhere(rigid_3 == 1)[0, :][1])
+            if rigid_right <= n+f[3]:
+                f[3] = rigid_right - n - 1
+        bomb_flame[m-f[0]:m+f[1]+1, n] = 1
+        bomb_flame[m, n-f[2]:n+f[3]+1] = 1
+
+        return bomb_flame
+
+    def update(self, gamma, batch_size):
         if self.learn_step_counter % 10 == 0:
             self.target_net.load_state_dict(self.eval_net.state_dict())
         self.learn_step_counter += 1
 
-        statesl, statesa, actions, rewards, next_statesl, next_statesa, done, epistep = self.buffer.sample(batch_size)
+        statesl, statesa, actions, rewards, next_statesl, next_statesa, done = self.buffer.sample(batch_size)
         #print(epistep)
-        
         #计算reward
-        computed_reward = []
-        for l, a, action, sl, sa, es in zip(next_statesl, next_statesa, actions, statesl, statesa, epistep):
-            computed_reward.append(self.reward(l, a, action[0], sl, sa, es))
+        compute_reward = []
+        for l, a, action, sl, sa, re in zip(next_statesl, next_statesa, actions, statesl, statesa, rewards):
+            compute_reward.append(self.reward(l, a, action, sl, sa, re))
         #这是得到的reward
-        computed_reward = torch.tensor(computed_reward)
-        action_index = actions.squeeze(-2)[:,0].unsqueeze(1)
+        computed_reward = torch.tensor(compute_reward)
+        #print(actions)
+
+        action_index = actions.squeeze(-2)#.unsqueeze(1)
         curr_Q_batch = self.eval_net(statesl,statesa)#[:,0]
         #print(curr_Q_batch)
         curr_Q = curr_Q_batch.gather(1, action_index.type(torch.int64)).squeeze(-1)
-        
+
         next_batch = self.target_net(next_statesl, next_statesa)#[:,0]
         next_Q = torch.max(next_batch,1)[0]
 
-        rewards_batch = rewards.squeeze(-2)[:,0]
+        #rewards_batch = rewards.squeeze(-2)[:,0]
+        rewards_batch = computed_reward
         #print(rewards_batch)
         # expected_Q = rewards + self.gamma * torch.max(next_Q, 1)
         #需要把done计算进去
@@ -191,40 +239,47 @@ def update(self, gamma, batch_size,episode, step):
         # expected_Q = done * (rewards + gamma * max_q_prime) + (1 - done) * 1 / (1 - gamma) * rewards
         # expected_Q = done * (rewards + gamma * max_q_prime) + 1 / (1 - gamma) * rewards
         loss = self.MSE_loss(curr_Q, expected_Q[0]) # TODO: try Huber Loss later too
-
         self.optim.zero_grad()
         loss.backward()
         self.optim.step()
 
+
     def epsdecay(self):
         self.epsilon = self.epsilon * self.eps_decay if self.epsilon > self.min_eps else self.epsilon
 
-    def compute_reward(self, local, additional, epistep):
-        m = self.buffer.get(tuple(epistep.tolist()))
-
-        return 0
-
+    def save_model(self):
+        torch.save({'dqnNet': self.eval_net.state_dict(),'optimizer_state_dict': self.optim.state_dict()}, 'model_dqn.pt')
+    
+    def load_model(self):
+        if os.path.exists('model_dqn.pt'):
+            state_dict = torch.load('model_dqn.pt')
+            self.eval_net.load_state_dict(state_dict['dqnNet'])
+            self.optim.load_state_dict(state_dict['optimizer_state_dict'])
+            self.target_net.load_state_dict(self.eval_net.state_dict())
 
 class Net1(nn.Module):
     def __init__(self):
+        # 初始化数组
         super(Net,self).__init__()
+        # 此步骤是官方要求
         """
         self.conv1=nn.Conv2d(199,16,2,stride=1,padding=1)
         self.conv2=nn.Conv2d(16,32,3,stride=1,padding=1)
         """
-        self.fc1 = nn.Linear(199,128)
+        self.fc1 = nn.Linear(199,128)   #设置输入层到隐藏层的函数
         self.fc1.weight.data.normal_(0, 0.1)
-        self.out = nn.Linear(128,6)
+        self.out = nn.Linear(128,6)     #设置隐藏层到输出层的函数
         self.out.weight.data.normal_(0, 0.1)
 
     def forward(self, x):
+        # 定义向前传播函数
         x = torch.FloatTensor(x)
         #x = torch.unsqueeze(x, dim=0).float()
         #x=self.conv1(x)
         #x=self.conv2(x)
         x = self.fc1(x)
-        x = F.relu(x)
-        out = self.out(x)
+        x = F.relu(x)  #给x加权成为a，用激励函数将a变成特征b
+        out = self.out(x)   #给b加权，预测最终结果
         return out
     
     
@@ -233,7 +288,7 @@ class Net(nn.Module):
     def __init__(self):
         super(Net,self).__init__()
         self.features = nn.Sequential(
-            nn.Conv2d(9,32,2,stride=1,padding=1),
+            nn.Conv2d(14,32,2,stride=1,padding=1),
             nn.ReLU(),
             nn.Conv2d(32,64,3,stride=1,padding=1),
             nn.ReLU(),
@@ -274,3 +329,4 @@ def forward1(self, lx, ax):
         out = self.fc(outx)
         return out
         
+
diff --git a/test/__pycache__/DQN2Agent.cpython-36.pyc b/test/__pycache__/DQN2Agent.cpython-36.pyc
index a41f4f4..6ec74e9 100644
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diff --git a/test/__pycache__/DQNAgent.cpython-36.pyc b/test/__pycache__/DQNAgent.cpython-36.pyc
index c056579..2ac0a33 100644
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diff --git a/test/__pycache__/plot.cpython-36.pyc b/test/__pycache__/plot.cpython-36.pyc
new file mode 100644
index 0000000..b28f4dd
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diff --git a/test/__pycache__/replay_buffer.cpython-36.pyc b/test/__pycache__/replay_buffer.cpython-36.pyc
index fd53247..cbd41d7 100644
Binary files a/test/__pycache__/replay_buffer.cpython-36.pyc and b/test/__pycache__/replay_buffer.cpython-36.pyc differ
diff --git a/test/__pycache__/utils.cpython-36.pyc b/test/__pycache__/utils.cpython-36.pyc
index ce756e8..90f47b7 100644
Binary files a/test/__pycache__/utils.cpython-36.pyc and b/test/__pycache__/utils.cpython-36.pyc differ
diff --git a/test/img/winrate.png b/test/img/winrate.png
new file mode 100644
index 0000000..a6d6848
Binary files /dev/null and b/test/img/winrate.png differ
diff --git a/test/main.py b/test/main.py
index e98e818..284a7d5 100644
--- a/test/main.py
+++ b/test/main.py
@@ -4,22 +4,26 @@
 import argparse
 import random
 import numpy as np
+import collections
 
 
 from pommerman import agents
 from pommerman.configs import one_vs_one_env
 from DQNAgent import DQNAgent
-from utils import featurize, CustomEnvWrapper
+from DQN2Agent import DQN2Agent
+from utils import featurize
+import os
+from plot import plot_win_rate
 
 
 def main():
     """解析参数"""
     parser = argparse.ArgumentParser(description='DQN pommerman MARL')
-    parser.add_argument('--episodes', type=int, default=3000, help='episodes')
+    parser.add_argument('--episodes', type=int, default=5000, help='episodes')
     parser.add_argument('--maxsteps', type=int, default=200, help='maximum steps')
     parser.add_argument('--showevery', type=int, default=1, help='report loss every n episodes')
 
-    parser.add_argument('--epsilon', type=float, default=0.05, help='parameter for epsilon greedy')
+    parser.add_argument('--epsilon', type=float, default=0.9, help='parameter for epsilon greedy')
     parser.add_argument('--eps_decay', type=float, default=0.995, help='epsilon decay rate')
     parser.add_argument('--min_eps', type=float, default=0.05, help='minimum epsilon for decaying')
     parser.add_argument('--gamma', type=float, default=0.95, help='gamma')
@@ -27,9 +31,10 @@ def main():
 
     parser.add_argument('--capacity', type=int, default=100000, help='capacity for replay buffer')
     parser.add_argument('--batch', type=int, default=201, help='batch size for replay buffer')
-    parser.add_argument('--tryepi', type=int, default=5, help='episode for agent to gain experience')
+    parser.add_argument('--tryepi', type=int, default=50, help='episode for agent to gain experience')
     parser.add_argument('--gpu', type=str, default='0', help='gpu number')
-
+    parser.add_argument('--win_in_epi', type=int, default='500', help='calculate win in epi..')
+    parser.add_argument('--ranepi', type=int, default='2000', help='agent go random action in epi..')
     args = parser.parse_args()
 
     # GPU
@@ -39,65 +44,96 @@ def main():
     agent_list = [agents.SimpleAgent(), agents.SimpleAgent()]  # placeholder
     env = pommerman.make('OneVsOne-v0', agent_list)
 
-    agent1 = DQNAgent(env, args)  # TODO: assertionerror; not agents.BaseAgent??
+    agent1 = DQN2Agent(env, args)  # TODO: assertionerror; not agents.BaseAgent??
     agent2 = agents.SimpleAgent()
 
     agent_list = [agent1, agent2]
     env = pommerman.make('OneVsOne-v0', agent_list)
 
-    episode_rewards = []
-    action_n = env.action_space.n
+    # plot
+    list_win = []
+
+    #episode_rewards = []
+    #action_n = env.action_space.n
 
-    win = 0
+    # 加载模型
+    #agent1.load_model()
+    # collect win times
+    if os.path.exists('model_dqn.pt'):
+        args.tryepi = 0
+        args.ranepi = 0
+        args.epsilon = 0.1
 
+    win_buffer = collections.deque(maxlen=args.win_in_epi)
     for episode in range(args.episodes):
         states = env.reset()
         done = False
-        episode_reward = 0
+        #episode_reward = 0
+        step = 0
         for step in range(args.maxsteps):
-            state_feature = featurize(env, states)
+            state_feature = featurize(env, states[0])
             # 刷新环境
-            if episode > (args.episodes - 10):
+            if episode % 100 == 0 and episode != 0:
                 env.render()
 
             # 选择action
-            if (episode <= args.tryepi) or (args.epsilon > random.random()):
-                actions = env.act(states)
-            else:
-                actions = env.act(states)
-                dqn_action = agent1.dqnact(state_feature)
-                actions[0] = int(np.int64(dqn_action))
+
+            actions = env.act(states)
+            dqn_action = agent1.dqnact(state_feature)
+            actions[0] = int(np.int64(dqn_action))
+                #print(actions[0])
+
             
             next_state, reward, done, info = env.step(actions)  # n-array with action for each agent
 
-            next_state_feature = featurize(env, next_state)
-            episode_reward += reward[0]
+            next_state_feature = featurize(env, next_state[0])
+            #episode_reward += reward[0]
             # 存储记忆
-            agent1.buffer.append([state_feature, actions, reward, next_state_feature, done], episode, step)
+            agent1.buffer.append([state_feature, actions[0], reward[0], next_state_feature, done])
             
             # 先走batch步之后再开始学习
-            if episode >= args.tryepi:
-                agent1.update(args.gamma, args.batch,episode, step)
+            if episode > args.tryepi and agent1.buffer.size() >= args.batch:
+                agent1.update(args.gamma, args.batch)
             
             # 更新state
             states = next_state
             
             if done:
                 break
-
+        '''
         if done:
             episode_rewards.append(episode_reward)
+        '''
         if episode % args.showevery == 0:
-            print(f"Episode: {episode + 1:2d} finished, result: {'Win' if 0 in info.get('winners', []) else 'Lose'}")
+            if 0 in info.get('winners', []):
+                print(f"Episode: {episode + 1:2d} finished, result: Win")
+            elif not done:
+                print(f"Episode: {episode + 1:2d} finished, result: Not finish")
+            else:
+                print(f"Episode: {episode + 1:2d} finished, result: Lose")
             #print(f"Avg Episode Reward: {np.mean(episode_rewards)}")
-        if 0 in info.get('winners', []) and episode > 500:
-            win += 1
-    
-        if episode > 500:
-            winrate = win / (episode - 500 + 1)
-            print(f"current winrate: {winrate}")
-
-        agent1.epsdecay()
+        
+        
+        if episode > args.tryepi:
+            agent1.epsdecay()
+            if 0 in info.get('winners', []):
+                win_buffer.append(1)
+            elif 1 in info.get('winners', []):
+                win_buffer.append(0)
+            if len(win_buffer) == args.win_in_epi:
+                avg = sum(win_buffer) / len(win_buffer)
+                print(f"current winrate: {avg}")
+                list_win.append(avg)
+                if len(list_win)%1000 == 0:
+                    plot_win_rate(list_win)
+
+
+
+
+
+        print('epsilon:',agent1.epsilon)
+
+    agent1.save_model()    #保存模型
 
     env.close()
 
diff --git a/test/model_dqn.pt b/test/model_dqn.pt
new file mode 100644
index 0000000..d49fe00
Binary files /dev/null and b/test/model_dqn.pt differ
diff --git a/test/model_dqn2.pt b/test/model_dqn2.pt
new file mode 100644
index 0000000..c3152c5
Binary files /dev/null and b/test/model_dqn2.pt differ
diff --git a/test/multi_main.py b/test/multi_main.py
index 5a99863..076ec0e 100644
--- a/test/multi_main.py
+++ b/test/multi_main.py
@@ -1,35 +1,40 @@
+import collections
 import pommerman
 import gym
 import torch
 import argparse
 import random
 import numpy as np
-
+import os
 
 from pommerman import agents
 from pommerman.configs import radio_v2_env, team_v0_fast_env, radio_competition_env
 from DQN2Agent import DQN2Agent
 from utils import featurize, CustomEnvWrapper, featurize2
+from plot import plot_win_rate
 
 
 def main():
     """解析参数"""
     parser = argparse.ArgumentParser(description='DQN pommerman MARL')
-    parser.add_argument('--episodes', type=int, default=3000, help='episodes')
-    parser.add_argument('--maxsteps', type=int, default=200, help='maximum steps')
+    parser.add_argument('--episodes', type=int, default=100, help='episodes')
+    parser.add_argument('--maxsteps', type=int, default=500, help='maximum steps')
     parser.add_argument('--showevery', type=int, default=1, help='report loss every n episodes')
 
-    parser.add_argument('--epsilon', type=float, default=0.05, help='parameter for epsilon greedy')
-    parser.add_argument('--eps_decay', type=float, default=0.995, help='epsilon decay rate')
+    parser.add_argument('--epsilon', type=float, default=0.5, help='parameter for epsilon greedy')
+    parser.add_argument('--eps_decay', type=float, default=0.99, help='epsilon decay rate')
     parser.add_argument('--min_eps', type=float, default=0.05, help='minimum epsilon for decaying')
     parser.add_argument('--gamma', type=float, default=0.95, help='gamma')
     parser.add_argument('--lr', type=float, default=0.01, help='learning rate')
+    # parser.add_argument('--lr_decay', type=float, default=0.99, help='learning rate decay rate')
+    # parser.add_argument('--lr_decay_s', type=float, default=100, help='learning rate decay rate setp size')
 
     parser.add_argument('--capacity', type=int, default=100000, help='capacity for replay buffer')
-    parser.add_argument('--batch', type=int, default=201, help='batch size for replay buffer')
-    parser.add_argument('--tryepi', type=int, default=5, help='episode for agent to gain experience')
+    parser.add_argument('--batch', type=int, default=256, help='batch size for replay buffer')
+    parser.add_argument('--tryepi', type=int, default=0, help='episode for agent to gain experience')
     parser.add_argument('--gpu', type=str, default='0', help='gpu number')
-
+    parser.add_argument('--win_in_epi', type=int, default=200, help='calculate win in epi..')
+    parser.add_argument('--ranepi', type=int, default=50, help='agent go random action in epi..')
     args = parser.parse_args()
 
     # GPU
@@ -41,72 +46,146 @@ def main():
 
     agent1 = DQN2Agent(env, args)  # TODO: assertionerror; not agents.BaseAgent??
     agent2 = agents.SimpleAgent()
-    agent3 = DQN2Agent(env, args)
+    # agent3 = DQN2Agent(env, args)
+    agent3 = agents.SimpleAgent()
     agent4 = agents.SimpleAgent()
 
     agent_list = [agent1, agent2, agent3, agent4]
     env = pommerman.make('PommeRadioCompetition-v2', agent_list)
 
+    # plot
+    list_win = []
+
     episode_rewards = []
     action_n = 6
 
-    win = 0
 
+    # 加载模型
+    #agent1.load_model()
+    # agent3.load_model()
+    # collect win times
+    # if os.path.exists('model_dqn2.pt'):
+    #     args.epsilon = 0.1
+    #     args.eps_decay = 0.98
+    #     args.tryepi = 0
+    #     args.ranepi = 0
+
+
+    win_buffer = collections.deque(maxlen=args.win_in_epi)
     for episode in range(args.episodes):
-        states = env.reset()  
+        # 固定地图
+        random.seed(2)
+        np.random.seed(2)
 
-        state_feature1 = featurize2(env, states, 0)
-        state_feature3 = featurize2(env, states, 2)
+        states = env.reset()  
+        # print('epi:', episode)
         done = False
         episode_reward = 0
+        die = 0
         for step in range(args.maxsteps):
+            state_feature1 = featurize2(env, states[0])
+            #state_feature3 = featurize2(env, states[2])
+            random.seed()
+            seed = random.random()
+            # print(seed)
+            # state_feature3 = featurize2(env, states[2])
             # 刷新环境
-            if episode > (args.episodes - 10):
-                env.render()
-            
+            # if episode % 100 == 0 and episode != 0:
+            #     env.render()
+            #env.render()
             # 选择action
-            if (args.epsilon > random.random()) or (episode <= args.tryepi):
+            if episode < args.tryepi:       # epi < 4
+                # print("simple try")
                 actions = env.act(states)
             else:
+                if args.epsilon > seed:
+                    actions = env.act(states)
+                else:
+                    actions = env.act(states)
+                    dqn_action1 = agent1.dqnact(state_feature1)
+                    actions[0] = int(np.int64(dqn_action1))
+
+                
+            """
+            elif episode < args.ranepi:     # epi < 3000
+                # print("simple try")
+                actions = env.act(states)
+            elif episode >= args.ranepi and args.epsilon > seed:
+            # elif episode >= args.ranepi:    # epi >= 3000 a
+                #print("random try")
+                actions = env.act(states)
+                actions[0] = random.randrange(0,6,1)
+                #actions[2] = random.randrange(0,6,1)
+            elif episode >= args.ranepi and args.epsilon <= seed:  # epi >= 3000 and eps <= random.random
+                #print("dqn select")
                 actions = env.act(states)
                 dqn_action1 = agent1.dqnact(state_feature1)
-                dqn_action3 = agent3.dqnact(state_feature3)
+                # dqn_action3 = agent3.dqnact(state_feature3)
                 actions[0] = int(np.int64(dqn_action1))
-                actions[2] = int(np.int64(dqn_action3))
+                #actions[2] = int(np.int64(dqn_action3))
+            """
             
+            # actions = env.act(states)
+
             next_state, reward, done, info = env.step(actions)  # n-array with action for each agent
-            next_state_feature1 = featurize2(env, next_state, 0)
-            next_state_feature3 = featurize2(env, next_state, 2)
-            episode_reward += reward[0]
+            if 10 not in next_state[0]['alive']:
+                info['winners'] = [1, 3]
+                reward = [-1, 1, -1, 1]
+            next_state_feature1 = featurize2(env, next_state[0])
+            # next_state_feature3 = featurize2(env, next_state[2])
+            #episode_reward += reward[0]
             # 存储记忆
-            agent1.buffer.append([state_feature1, actions, reward, next_state_feature1, done])
-            agent3.buffer.append([state_feature3, actions, reward, next_state_feature3, done])
+            agent1.buffer.append([state_feature1, actions[0], reward[0], next_state_feature1, done])
+            # agent3.buffer.append([state_feature3, actions[2], reward[2], next_state_feature3, done])
             # 先走batch步之后再开始学习
-            if agent1.buffer.size() > args.batch:
+            if agent1.buffer.size() >= args.batch:
+            # if agent1.buffer.size() >= args.batch:
                 agent1.update(args.gamma, args.batch)
-            if agent3.buffer.size() > args.batch:
-                agent3.update(args.gamma, args.batch)
+            # if episode > args.tryepi and agent1.buffer.size() >= args.batch:
+            #     agent3.update(args.gamma, args.batch)
             # 更新state
             states = next_state
-            
+
+            #print('alive:', next_state[0]['alive'])
+
             if done:
                 break
 
-        if done:
-            episode_rewards.append(episode_reward)
+            # agent1 die -> game over
+            if 10 not in next_state[0]['alive']:
+                break
+
+        #if done:
+        #    episode_rewards.append(episode_reward)
         if episode % args.showevery == 0:
-            print(f"Episode: {episode + 1:2d} finished, result: {'Win' if 0 in info.get('winners', []) else 'Lose'}")
-            print(f"Avg Episode Reward: {np.mean(episode_rewards)}")
-        if 0 in info.get('winners', []) and episode > 500:
-            win += 1
-    
-        if episode > 500:
-            winrate = win / (episode + 1)
-            print(f"current winrate: {winrate}")
+            if 0 in info.get('winners', []):
+                print(f"Episode: {episode + 1:2d} finished, result: Win")
+            elif 1 in info.get('winners', []):
+                print(f"Episode: {episode + 1:2d} finished, result: Lose")
+            else:
+                print(f"Episode: {episode + 1:2d} finished, result: Not finish")
+            #print(f"Avg Episode Reward: {np.mean(episode_rewards)}")
+        
+        if episode > args.win_in_epi:
+            agent1.epsdecay()
+            if 0 in info.get('winners', []):
+                win_buffer.append(1)
+            elif 1 in info.get('winners', []):
+                win_buffer.append(0)
+            if len(win_buffer) == args.win_in_epi:
+                avg = sum(win_buffer) / len(win_buffer)
+                print(f"current winrate: {avg}")
+                list_win.append(avg)
+                # if len(list_win) % 500 == 0:
+                #     plot_win_rate(list_win)
+
+
 
         agent1.epsdecay()
-        agent3.epsdecay()
+        # agent3.epsdecay()
 
+    agent1.save_model()
+    # agent3.save_model()
     env.close()
 
     # TODO: Implement Target Network
diff --git a/test/plot.py b/test/plot.py
new file mode 100644
index 0000000..1117eb7
--- /dev/null
+++ b/test/plot.py
@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+
+
+def get_png_path(png_name):
+    folder_path = os.getcwd().replace('\\', '/')
+    png_path = os.path.join(folder_path,'img',png_name)
+    return png_path
+
+def plot(list,title,png_name):
+    s = len(list)
+    x = [i + 1 for i in range(s)]
+    y = list
+
+    plt.figure(1)
+    # plt.axis([1, s, 0, 1])
+    plt.title(title)
+    plt.plot(x, y)
+
+    png_path = get_png_path(png_name)
+    plt.savefig(png_path)
+    plt.show()
+
+def plot_win_rate(list_win_rate):
+    return plot(list_win_rate, 'Win rate', 'winrate.png')
+
+def plot_reward(list_reward):
+    return plot(list_reward, 'Q-value', 'Q_value.png')
+
+
+if __name__ == '__main__':
+
+    list_win_rate = [i*0.001/5 for i in range(500)]
+    print(list_win_rate)
+    plot_win_rate(list_win_rate)
+    print('------------------------')
+
+    list_reward = [i*0.001/5 for i in range(500)]
+    print(list_reward)
+    plot_reward(list_reward)
+
+
+
+
diff --git a/test/replay_buffer.py b/test/replay_buffer.py
index 6c7953c..b361287 100644
--- a/test/replay_buffer.py
+++ b/test/replay_buffer.py
@@ -48,30 +48,31 @@ def sample2(self, batch_size):
         state, action, reward, next_state, done = zip(*random.sample(self.buffer, batch_size))
         return np.concatenate(state), action, reward, np.concatenate(next_state), done
 
+
 class ReplayBuffer2():
     def __init__(self, buffer_limit):
-        #self.buffer = collections.deque(maxlen=buffer_limit)
-        self.limit = buffer_limit
-        self.memory = {}
+        self.buffer = collections.deque(maxlen=buffer_limit)
+        # self.limit = buffer_limit
+        # self.memory = {}
 
-    def append(self, transition, episode, step):
-        #self.buffer.append(transition)
-        key = (episode, step)
-        self.memory[key] = transition
-        if len(self.memory) > self.limit:
-            key = self.memory.keys()[0]
-            self.memory.pop(key)
+    def append(self, transition):
+        self.buffer.append(transition)
+        # key = (episode, step)
+        # self.memory[key] = transition
+        # if len(self.memory) > self.limit:
+        #    key = self.memory.keys()[0]
+        #    self.memory.pop(key)
 
-    def get(self, key):
-        return self.memory.get(key)
+    # def get(self, key):
+    #    return self.memory.get(key)
 
     def sample(self, n):
-        #mini_batch = random.sample(self.buffer, n)
-        mini_batch = random.sample(self.memory.keys(), n)
+        mini_batch = random.sample(self.buffer, n)
+
         sl_lst, sa_lst, a_lst, r_lst, sl_prime_list, sa_prime_list, done_mask_list = [], [], [], [], [], [], []
-        epistep = []
-        for key in mini_batch:   #transition: tuple
-            transition = self.memory.get(key)
+
+        for transition in mini_batch:  # transition: tuple
+            # transition = self.memory.get(key)
             s, a, r, s_prime, done_mask = transition
             sl_lst.append(s['local'])
             sa_lst.append(s['additional'])
@@ -80,7 +81,6 @@ def sample(self, n):
             sl_prime_list.append(s_prime['local'])
             sa_prime_list.append(s_prime['additional'])
             done_mask_list.append([done_mask])
-            epistep.append(key)
         """
         sl_lst = np.array(sl_lst)
         sa_lst = np.array(sa_lst)
@@ -95,13 +95,13 @@ def sample(self, n):
                 torch.tensor(a_lst), torch.tensor(r_lst),
                 torch.tensor(sl_prime_list, dtype=torch.float),
                 torch.tensor(sa_prime_list, dtype=torch.float),
-                torch.tensor(done_mask_list), torch.tensor(epistep))
-    
+                torch.tensor(done_mask_list))
+
     def sample2(self, n):
         mini_batch = random.sample(self.buffer, n)
         s_lst, a_lst, r_lst, s_prime_list, done_mask_list = [], [], [], [], []
 
-        for transition in mini_batch:   #transition: tuple
+        for transition in mini_batch:  # transition: tuple
             s, a, r, s_prime, done_mask = transition
             s_lst.append([s])
             a_lst.append([a])
@@ -115,4 +115,4 @@ def sample2(self, n):
                 torch.tensor(done_mask_list))
 
     def size(self):
-        return len(self.buffer)
\ No newline at end of file
+        return len(self.buffer)
diff --git a/test/utils.py b/test/utils.py
index bf29d49..e390adb 100644
--- a/test/utils.py
+++ b/test/utils.py
@@ -87,7 +87,7 @@ def _preprocessing(self, obs: List[Dict], **kwargs) -> List[Dict]:
                         pos = np.array(d.get('position'))
                         view_range = 2 * self._agent_view_size + 1
                         v = v[pos[0]:pos[0] + view_range,
-                              pos[1]:pos[1] + view_range]
+                            pos[1]:pos[1] + view_range]
 
                     locational.append(v)
 
@@ -106,6 +106,115 @@ def _preprocessing(self, obs: List[Dict], **kwargs) -> List[Dict]:
         return out
 
 
+def rebuild_board2(board):
+    # 将board中数据分离，2D化
+    rigid = []
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 1:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        rigid.append(new_row)
+
+    wood = []
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 2:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        wood.append(new_row)
+
+    bomb = []
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 3:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        bomb.append(new_row)
+
+    flame = []
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 4:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        flame.append(new_row)
+
+    # 暂时用不到fog
+    fog = []
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 4:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        fog.append(new_row)
+
+    power_up = []
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 6 or num == 7 or num == 8:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        power_up.append(new_row)
+
+    agent1 = []
+    # 如果是10为此处为agent,则取1.0
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 10:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        agent1.append(new_row)
+
+    agent2 = []
+    # 如果是11为此处为agent,则取1.0
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 11:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        agent2.append(new_row)
+
+    agent3 = []
+    # 如果是12为此处为agent,则取1.0
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 12:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        agent3.append(new_row)
+
+    agent4 = []
+    # 如果是13为此处为agent,则取1.0
+    for row in board:
+        new_row = []
+        for num in row:
+            if num == 13:
+                new_row.append(1.0)
+            else:
+                new_row.append(0.0)
+        agent4.append(new_row)
+
+    return rigid, wood, bomb, power_up, fog, agent1, agent2, agent3, agent4, flame
+
 
 def featurize(env, states):
     '''
@@ -117,11 +226,11 @@ def featurize(env, states):
     Output:
     - feature: list[num_agents, 372]
     '''
-    
-    #length = len(env.featurize(states[0]).tolist())
-    #list = env.featurize(states[0]).tolist()
+
+    # length = len(env.featurize(states[0]).tolist())
+    # list = env.featurize(states[0]).tolist()
+    # states = states[0]
     local = featurize2D(states)
-    states = states[0]
     """
     board = states["board"].reshape(-1).astype(np.float32)
     bomb_blast_strength = states["bomb_blast_strength"].reshape(-1).astype(np.float32)
@@ -135,15 +244,14 @@ def featurize(env, states):
     local.append(flame_life.tolist())
     """
     feature = {'local': local}
-
     additional = []
     position = utility.make_np_float(states["position"])
-    ammo = utility.make_np_float([states["ammo"]])  #fff
+    ammo = utility.make_np_float([states["ammo"]])  # fff
     blast_strength = utility.make_np_float([states["blast_strength"]])
     can_kick = utility.make_np_float([states["can_kick"]])
     teammate = utility.make_np_float([states["teammate"].value])
     enemies = utility.make_np_float([e.value for e in states["enemies"]])
-    #print(position, ammo, blast_strength, can_kick, teammate, enemies)
+    # print(position, ammo, blast_strength, can_kick, teammate, enemies)
     """
     additional.append(position.tolist())
     additional.append(ammo.tolist())
@@ -152,28 +260,30 @@ def featurize(env, states):
     additional.append(teammate.tolist())
     additional.append(enemies.tolist())
     """
-    #print(additional)
-    #position占两个数，所以你要取ammo的话就要取additional[2]
+    # print(additional)
+    # position占两个数，所以你要取ammo的话就要取additional[2]
     additional = np.concatenate(
-            (position, ammo,
-             blast_strength, can_kick, teammate, enemies))
+        (position, ammo,
+         blast_strength, can_kick, teammate, enemies))
 
     feature['additional'] = additional.tolist()
     return feature
 
+
 def featurize2D(states):
     feature2D = []
-    # 共9个矩阵
-    for board in rebuild_board(states[0]["board"]):
+    # 共10个矩阵
+    for board in rebuild_board2(states["board"]):
         feature2D.append(board)
 
-    feature2D.append(states[0]["bomb_blast_strength"].tolist())
-    feature2D.append(states[0]["bomb_life"].tolist())
-    feature2D.append(states[0]["bomb_moving_direction"].tolist())
-    feature2D.append(states[0]["flame_life"].tolist())
+    feature2D.append(states["bomb_blast_strength"].tolist())
+    feature2D.append(states["bomb_life"].tolist())
+    feature2D.append(states["bomb_moving_direction"].tolist())
+    feature2D.append(states["flame_life"].tolist())
 
     return feature2D
 
+
 def rebuild_board(board):
     # 将board中数据分离，2D化
     rigid = []
@@ -215,9 +325,8 @@ def rebuild_board(board):
             else:
                 new_row.append(0.0)
         flame.append(new_row)
-    """
-    暂时用不到fog
-    fog =[]
+
+    fog = []
     for row in board:
         new_row = []
         for num in row:
@@ -226,7 +335,7 @@ def rebuild_board(board):
             else:
                 new_row.append(0.0)
         fog.append(new_row)
-    """
+
     power_up = []
     for row in board:
         new_row = []
@@ -248,17 +357,18 @@ def rebuild_board(board):
                 new_row.append(0.0)
         agents.append(new_row)
 
-    return rigid, wood, bomb, power_up, agents
+    return rigid, wood, bomb, power_up, fog, agents, flame
 
 
-def featurize2(env, states, aid):
+def featurize2(env, states):
     """
     feature = []
     for state in states:
         feature.append((env.featurize(state)).tolist())
     """
     local = []
-    states = states[aid]
+    # print(states)
+    '''
     board = states["board"].reshape(-1).astype(np.float32)
     bomb_blast_strength = states["bomb_blast_strength"].reshape(-1).astype(np.float32)
     bomb_life = states["bomb_life"].reshape(-1).astype(np.float32)
@@ -270,7 +380,9 @@ def featurize2(env, states, aid):
     local.append(bomb_moving_direction.tolist())
     local.append(flame_life.tolist())
     feature = []
-
+    '''
+    local = featurize2D(states)
+    feature = {'local': local}
     additional = []
     position = utility.make_np_float(states["position"])
     ammo = utility.make_np_float([states["ammo"]])
@@ -278,9 +390,13 @@ def featurize2(env, states, aid):
     can_kick = utility.make_np_float([states["can_kick"]])
     teammate = utility.make_np_float([states["teammate"].value])
     enemies = utility.make_np_float([e.value for e in states["enemies"]])
-    additional = np.concatenate((position, ammo, blast_strength, can_kick, teammate, enemies))
     message = states['message']
     message = utility.make_np_float(message)
-    return np.concatenate(
-            (board, bomb_blast_strength, bomb_life, position, ammo,
-             blast_strength, can_kick, teammate, enemies, message)).tolist()
+    additional = np.concatenate(
+        (position, ammo,
+         blast_strength, can_kick, teammate, enemies, message))
+
+    feature['additional'] = additional.tolist()
+
+    # feature['alive'] = states['alive']
+    return feature