强化学习教父 Richard Sutton 的经典教材《Reinforcement Learning：An Introduction》第二版配套代码，本书分为三大部分，共十七章，对其简介和框架做了扼要介绍

强化学习导论第二版，网上虽已有，但有80M之多。这个版本仅10M多。

文件中包含了强化学习精要的代码，学习强化学习精要必须拥有，同时还包含了deepmind团队davidsilver公开课PPT，供大家学习。

The twenty years since the publication of the first edition of this book have seen tremendous progress in artificial intelligence, propelled in large part by advances in machine learning, including advances in reinforcement learning. Although the impressive computational power that became available is responsible for some of these advances, new developments in theory and algorithms have been driving forces as well. In the face of this progress, a second edition of our 1998 book was long overdue, and we finally began the project in 2012. Our goal for the second edition was the same as our goal for the first: to provide a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. The edition remains an introduction, and we retain a focus on core, online learning algorithms. This edition includes some new topics that rose to importance over the intervening years, and we expanded coverage of topics that we now understand better. But we made no attempt to provide comprehensive coverage of the field, which has exploded in many di↵erent directions. We apologize for having to leave out all but a handful of these contributions.

Reinforcement Learning：An Introduction PDF文档附本书源代码

强化学习简单实现 开发语言：C++ 运行环境：Ubuntu 16.06

该文件为莫烦python网课中reinforcement learning部分的代码

Multi-Agent Machine Learning A Reinforcement Approach

This is a draft by Dimitri P. Bertsekas, who from MIT. It may be published in 2019 by Athena Scientific. It is a good resource to study RL and Opt.

Q-learning 实现Atari game - taxiv2，基于gym model。