From 998a705b341a9483174a4418f5f7cd62e7e204ab Mon Sep 17 00:00:00 2001
From: Xcreate672 <3534270192@qq.com>
Date: Sat, 6 Jun 2026 15:51:33 +0800
Subject: [PATCH 1/3] =?UTF-8?q?=E6=94=B9=E6=88=90=E5=AF=B9=E8=B1=A1?=
 =?UTF-8?q?=E6=A0=BC=E5=BC=8F=20Update=20ignore=5Fusers.json?=
MIME-Version: 1.0
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改成对象格式
---
 ignore_users.json | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/ignore_users.json b/ignore_users.json
index ed695fe839..584595373e 100644
--- a/ignore_users.json
+++ b/ignore_users.json
@@ -1,5 +1,8 @@
 [
-    "Haidong Wang",
-    "donghaiwang",
-    "whd@hutb.edu.cn"
-]
\ No newline at end of file
+    {
+        "name": "Haidong Wang",
+        "github": "donghaiwang",
+        "email": "whd@hutb.edu.cn",
+        "role": "author"
+    }
+]

From 97797c0c66da04312ff80b24a22c7732fdd6253e Mon Sep 17 00:00:00 2001
From: Xcreate672 <3534270192@qq.com>
Date: Tue, 9 Jun 2026 15:10:59 +0800
Subject: [PATCH 2/3] =?UTF-8?q?Update=20gaussian=5Fmixture.md=E8=A1=A5?=
 =?UTF-8?q?=E5=85=85=E9=AB=98=E6=96=AF=E6=B7=B7=E5=90=88=E6=A8=A1=E5=9E=8B?=
 =?UTF-8?q?=E5=85=AC=E5=BC=8F=E4=B8=8E=E4=BB=A3=E7=A0=81=E7=A4=BA=E4=BE=8B?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

补充高斯混合模型公式与代码示例
---
 docs/gaussian_mixture.md | 31 +++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)

diff --git a/docs/gaussian_mixture.md b/docs/gaussian_mixture.md
index 4f2ea7cbce..5bc0e45fb0 100644
--- a/docs/gaussian_mixture.md
+++ b/docs/gaussian_mixture.md
@@ -44,3 +44,34 @@
 - 异常检测
 - 图像分割
 - 语音识别
+- 
+## 数学公式
+
+GMM 的概率密度函数为：
+
+`p(x) = Σ π_k * N(x | μ_k, Σ_k)`
+
+其中：
+- K 为高斯成分数量
+- π_k 为第 k 个成分的混合权重，满足 Σπ_k = 1
+- N(x | μ_k, Σ_k) 为第 k 个高斯分布
+
+## 代码示例
+
+使用 scikit-learn 拟合高斯混合模型：
+
+```python
+from sklearn.mixture import GaussianMixture
+import numpy as np
+
+# 生成示例数据
+X = np.random.randn(300, 2)
+
+# 创建并训练模型
+gmm = GaussianMixture(n_components=3, random_state=0)
+gmm.fit(X)
+
+# 预测类别
+labels = gmm.predict(X)
+print("各成分权重:", gmm.weights_)
+```

From f7ca7e7347499f34e5afe1647e08fb75945de00d Mon Sep 17 00:00:00 2001
From: Xcreate672 <3534270192@qq.com>
Date: Wed, 10 Jun 2026 17:20:32 +0800
Subject: [PATCH 3/3] =?UTF-8?q?=E8=A1=A5=E5=85=85=E5=BC=BA=E5=8C=96?=
 =?UTF-8?q?=E5=AD=A6=E4=B9=A0DQN=E4=BB=A3=E7=A0=81=E7=A4=BA=E4=BE=8B?=
 =?UTF-8?q?=E4=B8=8ECarla=E5=BA=94=E7=94=A8=20Update=20RL.md?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
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补充强化学习DQN代码示例与Carla应用
---
 docs/RL.md | 35 +++++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)

diff --git a/docs/RL.md b/docs/RL.md
index 2b7b80a739..c66d7a21f8 100644
--- a/docs/RL.md
+++ b/docs/RL.md
@@ -58,3 +58,38 @@
 - 自动驾驶
 - 推荐系统
 - 资源调度
+  
+## 代码示例
+
+使用 PyTorch 实现 DQN 网络：
+
+```python
+import torch
+import torch.nn as nn
+
+class DQN(nn.Module):
+    """深度Q网络"""
+    def __init__(self, state_dim, action_dim):
+        super(DQN, self).__init__()
+        self.net = nn.Sequential(
+            nn.Linear(state_dim, 128),
+            nn.ReLU(),
+            nn.Linear(128, 128),
+            nn.ReLU(),
+            nn.Linear(128, action_dim)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+```
+
+## 在 Carla 中的应用
+
+- **车道保持**：以偏离车道线距离作为负奖励
+- **避障**：碰撞时给予大额负奖励，安全行驶给予正奖励
+- **速度控制**：以实际速度与目标速度差值作为奖励信号
+
+## 参考资料
+
+- [PyTorch 强化学习教程](https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html)
+- [OpenAI Gym 文档](https://gymnasium.farama.org/)