/rl-book

Source codes for the book "Reinforcement Learning: Theory and Python Implementation"

Primary LanguageJupyter Notebook

强化学习:原理与Python实现

世界上第一本配套 TensorFlow 2 代码的强化学习教程书

**第一本配套 TensorFlow 2 代码的纸质算法书

Book

本书介绍强化学习理论及其 Python 实现。

  • 理论完备:全书用一套完整的数学体系,严谨地讲授强化学习的理论基础,主要定理均给出证明过程。各章内容循序渐进,覆盖了所有主流强化学习算法,包括资格迹等非深度强化学习算法和柔性执行者/评论者等深度强化学习算法。
  • 案例丰富:在您最爱的操作系统(包括 Windows、macOS、Linux)上,基于最新的 Python 3.7、Gym 0.14 和 TensorFlow 2(兼容 TensorFlow 1),实现强化学习算法。全书实现统一规范,体积小、重量轻。第 1~9 章给出了算法的配套实现,环境部分只依赖于 Gym 的最小安装,在没有 GPU 的计算机上也可运行;第 10~12 章介绍了多个热门综合案例,涵盖 Gym 的完整安装和自定义扩展,在有普通 GPU 的计算机上即可运行。

目录

  1. 初识强化学习
  2. Markov决策过程
  3. 有模型数值迭代
  4. 回合更新价值迭代
  5. 时序差分价值迭代
  6. 函数近似方法
  7. 回合更新策略梯度方法
  8. 执行者/评论者方法
  9. 连续动作空间的确定性策略
  10. 综合案例:电动游戏
  11. 综合案例:棋盘游戏
  12. 综合案例:自动驾驶

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勘误列表: https://github.com/zhiqingxiao/rl-book/wiki/errata

Reinforcement Learning: Theory and Python Implementation

The First Reinforcement Learning Tutorial Book with TensorFlow 2 Implementation

This is a tutorial book on reinforcement learning, with explanation of theory and Python implementation.

  • Theory: Starting from a uniform mathematical framework, this book derives the theory and algorithms of reinforcement learning, including all major algorithms such as eligibility traces and soft actor-critic algithms.
  • Practice: Every chapter is accompanied by high quality implementation based on Python 3.7, Gym 0.14, and Tensorflow 2.

Table of Contents

  1. Introduction of Reinforcement Learning
  2. Markov Decision Process
  3. Model-based Numeric Iteration
  4. Monte-Carlo Learning
  5. Temporal Difference Learning
  6. Function Approximation
  7. Policy Gradient
  8. Actor-Critic
  9. Deterministic Policy Gradient
  10. Case Study: Video Game
  11. Case Study: Board Game
  12. Case Study: Self-Driving Car

BibTeX

@book{xiao2019,
 title     = {Reinforcement Learning: Theory and {Python} Implementation},
 author    = {Zhiqing Xiao}
 year      = 2019,
 month     = 7,
 publisher = {China Machine Press},
}