RL-Study/README.md

# RL-Study

强化学习算法实现与学习笔记，基于赵世钰老师《Mathematical Foundations of Reinforcement Learning》。

## 项目结构

```
RL-Study/
├── Lecture slides/           # 课程幻灯片
│   ├── slidesForMyLectureVideos/  # 配套视频课件
│   └── slidesContinuouslyUpdated/ # 持续更新的课件
├── Notebooks/               # Jupyter 学习笔记
│   ├── C1.ipynb ~ C10.ipynb  # 各章节推导与实验
│   ├── SAC.ipynb            # SAC (Soft Actor-Critic) 算法
│   └── *_training_results.png  # 训练结果可视化
├── RawBook/                 # 原书资源
├── RL_Algothrithms/         # 核心算法实现
│   ├── agents/              # 智能体实现
│   │   ├── a2c.py           # A2C (Advantage Actor-Critic)
│   │   ├── qac.py           # QAC (Q-Value Actor-Critic)
│   │   ├── off_pac.py       # Off-PAC (Off-Policy Actor-Critic)
│   │   ├── ddpg.py          # DDPG (Deep Deterministic Policy Gradient)
│   │   └── dpac.py          # DPAC (Deterministic Policy Actor-Critic)
│   ├── networks.py          # 离散动作空间网络 (Actor, QCritic, VCritic)
│   ├── networks_cont.py     # 连续动作空间网络 (ContActor, ContQCritic)
│   ├── utils.py             # 工具函数
│   ├── main.py              # 离散动作空间训练入口 (CartPole-v1)
│   ├── disp_main.py         # 离散动作空间多算法对比
│   └── cont_main.py         # 连续动作空间训练入口 (Pendulum-v1)
└── TRPO/                    # TRPO (Trust Region Policy Optimization) 独立实现
    ├── models.py            # ActorNet (高斯策略), CriticNet (值函数)
    ├── utils.py             # RolloutBuffer, GAE, 共轭梯度, FVP
    ├── agent.py             # TRPO 智能体
    └── main.py              # TRPO 训练入口 (Pendulum-v1)
```

## 已实现算法

### 离散动作空间（CartPole-v1）

| 算法    | 文件                                                   | 说明                                                   |
| ------- | ------------------------------------------------------ | ------------------------------------------------------ |
| A2C     | [agents/a2c.py](RL_Algothrithms/agents/a2c.py)         | Advantage Actor-Critic，同步版本，V-critic，带熵正则化 |
| QAC     | [agents/qac.py](RL_Algothrithms/agents/qac.py)         | Q-Value Actor-Critic，On-policy SARSA 风格，支持 GPU   |
| Off-PAC | [agents/off_pac.py](RL_Algothrithms/agents/off_pac.py) | Off-Policy Actor-Critic，带重要性采样，epsilon 探索    |

### 连续动作空间（Pendulum-v1）

| 算法 | 文件                                             | 说明                                                           |
| ---- | ------------------------------------------------ | -------------------------------------------------------------- |
| DDPG | [agents/ddpg.py](RL_Algothrithms/agents/ddpg.py) | Deep Deterministic Policy Gradient，离策略，带目标网络和软更新 |
| DPAC | [agents/dpac.py](RL_Algothrithms/agents/dpac.py) | Deterministic Policy Actor-Critic，在策略                      |
| TRPO | [agent.py](TRPO/agent.py)                        | Trust Region Policy Optimization，共轭梯度法 + 线搜索 + GAE    |

## 环境配置

```bash
pip install torch numpy matplotlib gymnasium
```

## 快速开始

### 离散动作空间（CartPole-v1）

```bash
cd RL_Algothrithms
python main.py --agent a2c    # 训练 A2C
python main.py --agent qac     # 训练 QAC
python disp_main.py           # 多算法对比训练
```

### 连续动作空间（Pendulum-v1）

```bash
cd RL_Algothrithms
python cont_main.py           # 训练 DDPG 和 DPAC
```

### TRPO

```bash
cd TRPO
python main.py                # 训练 TRPO
```

## 关于原书

- **书名**: Mathematical Foundations of Reinforcement Learning
- **作者**: Shiyu Zhao (Westlake University)
- **GitHub**: [MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning](https://github.com/MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning)
- **B站**: [赵世钰老师频道](https://space.bilibili.com/2044042934)
- **YouTube**: [课程列表](https://youtube.com/playlist?list=PLEhdbSEZZbDaFWPX4gehhwB9vJZJ1DNm8)

## License

MIT License（代码部分）