Initial commit: Add reinforcement learning study materials and grid world code
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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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# Jupyter Notebook
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.ipynb_checkpoints
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*/.ipynb_checkpoints/*
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# Distribution / Packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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# Virtual Environments
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venv/
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env/
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.env
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.venv/
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ENV/
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env.bak/
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venv.bak/
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# IDEs
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.vscode/
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.idea/
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# OS specific files
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.DS_Store
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Thumbs.db
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# Byte-compiled / optimized / DLL files
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*.pyc
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*.pyd
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My lecture slides are put into two folders.
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- The folder "slidesForMyLectureVideos" contains all **the slides that I used to record my lecture videos**.
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- The folder "slidesContinuouslyUpdated" contains **the slides that I updated continuously**.
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The slides in the two folders are very similar, but there are some minor differences, such as typo correction and content adjustment.
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**If you are not studying my online lecture videos, I suggest you check the slides in the slidesContinuouslyUpdated folder since they have been improved continuously.**
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# RL-Study
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# 强化学习的数学基础
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用于学习RL的相关知识,参考书目《强化学习的数学方法》
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本项目是我在学习赵世钰老师书籍 **《Mathematical Foundations of Reinforcement Learning》** 时建立的个人仓库。主要用于记录学习笔记、公式推导,以及使用 Python/Jupyter Notebook 复现书中的算法和 Grid World 示例。
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## 📚 关于原书 (Original Book)
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本项目的核心内容基于赵世钰老师的开源书籍,以下是原书的相关信息:
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- **书名**: Mathematical Foundations of Reinforcement Learning
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- **作者**: Shiyu Zhao (Westlake University)
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- **GitHub 仓库**: [Book-Mathematical-Foundation-of-Reinforcement-Learning](https://github.com/MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning)
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- **配套视频**:
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- [Bilibili (中文)](https://space.bilibili.com/2044042934)
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- [YouTube (English)](https://youtube.com/playlist?list=PLEhdbSEZZbDaFWPX4gehhwB9vJZJ1DNm8)
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---
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## 📂 项目结构
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本项目主要包含以下内容:
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- `Code for grid world/`:
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- `python_version/`: 使用 Python 复现的 Grid World 环境及相关算法。
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- `matlab_version/`: (可选) 原书提供的 Matlab 参考代码。
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- `Lecture slides/`: 课程相关的幻灯片或我的注解。
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- `Notebooks/`: (计划中) 包含详细推导和实验过程的 Jupyter Notebooks。
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## 🛠️ 环境配置
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本项目使用 Python 进行开发。推荐使用 `uv` 或 `conda` 管理环境。
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```bash
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# 安装依赖 (示例)
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pip install numpy matplotlib jupyter
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```
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## 📝 开源协议 (License)
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### 关于代码
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本项目中由本人编写的复现代码遵循 **MIT License** 开源协议。这意味着你可以自由地使用、修改和分发这些代码,但请保留原作者的版权声明。
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### 关于笔记
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项目中的学习笔记内容仅供个人学习交流使用。
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---
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## 🤝 致谢
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特别感谢赵世钰老师提供的精彩教材和开源资源,帮助我们深入理解强化学习的数学原理。
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