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Learning Resources And Links Of Reinforcement Learning （updating）
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# awesome-reinforcement-learning
强化学习的相关学习资源、链接。
本仓库含有强化学习最基础的实现，和部分强化学习经典书籍资源。
- 强化学习各个代码实现，相对于网络上其它实现最大的优势在于算法实现的框架遵循最简洁的原则，不同算法之间的实现差异只体现在算法最核心的不同点。能够非常快速地帮助初学者了解各个算法之间的差异。
# 网址教程资源
### 教程网址
- [强化学习系列教程](http://www.algorithmdog.com/series/rl-series)
- [强化学习知识大讲堂](https://zhuanlan.zhihu.com/sharerl)
- [南京大学俞扬博士万字演讲全文：强化学习前沿](https://www.leiphone.com/news/201705/NlTc7oObBqh116Z5.html)
- [OpenAi Spinning Up](https://spinningup.openai.com/en/latest/index.html)
- [David Silver《深度强化学习》公开课教程学习笔记以及实战](https://zhuanlan.zhihu.com/reinforce)
### Book
- [Hands-On Reinforcement Learning With Python](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python)
- [Reinforcement Learning: Theory and Python Implementation](https://github.com/ZhiqingXiao/rl-book)
- [An Introduction to Deep Reinforcement Learning](https://arxiv.org/abs/1811.12560v2)
- [Foundations and Trends® in Machine Learning](https://www.nowpublishers.com/MAL)
- [REINFORCEMENT LEARNING AND OPTIMAL CONTROL](http://web.mit.edu/dimitrib/www/RLbook.html)
### Video Course
- [CS 294: Deep Reinforcement Learning](http://rll.berkeley.edu/deeprlcourse/)；
- [中文翻译-2018秋季CS294-112深度强化学习](https://ai.yanxishe.com/page/groupDetail/30?invitation=&from=channel_share)；
- [CS294课程中文笔记-1](https://zhuanlan.zhihu.com/c_125238795)；
- [CS294课程中文笔记-2 (记录较好)](https://zhuanlan.zhihu.com/c_150977189);
- [作业讲解](https://zhuanlan.zhihu.com/codekitty)
- [David Silver's course](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html);
- [David视频里所使用的讲义pdf](https://github.com/18279406017/Course-notes/tree/master/Reinforcement%20Learning/PPT%20OF%20David%20Silver)
- [John Schulmann's lectures](https://www.youtube.com/channel/UCBOEQxX6zdihFB3VxxJdgHg/videos)
- [Deep RL Bootcamp](https://sites.google.com/view/deep-rl-bootcamp/lectures)
- [CS 287: Advanced Robotics, Fall 2015 ](https://people.eecs.berkeley.edu/~pabbeel/cs287-fa15/#syllabus)
- [CS234: Reinforcement Learning Winter 2019](http://web.stanford.edu/class/cs234/index.html)
- [Deep Learning (DLSS) and Reinforcement Learning (RLSS) Summer School, Montreal 2017](http://videolectures.net/deeplearning2017_montreal/)
- [Advanced Deep Learning and Reinforcement Learning](https://github.com/enggen/DeepMind-Advanced-Deep-Learning-and-Reinforcement-Learning)
- [强化学习教程(莫烦)](https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/)
### 博客网址
- [Play pong with deep reinforcement learning based on pixel]( http://karpathy.github.io/2016/05/31/rl/)
- [Deep Learning in a Nutshell: Reinforcement Learning](https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-reinforcement-learning/)
- [AlphaGo](https://deepmind.com/research/alphago/)
### 领域专家
- [加州大学伯克利分校机器人学专家 Sergey Levine](https://people.eecs.berkeley.edu/~svlevine/)
- [前百度首席科学家 Andrew Ng](http://www.andrewng.org/)
- [加拿大阿尔伯塔大学著名增强学习大师Richard S. Sutton 教授](https://www.amii.ca/sutton/)
- [Google DeepMind AlphaGo项目的主程序员 David Silver 博士](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Home.html)
- [机器博弈专家Tuomas Sandholm教授](http://www.cs.cmu.edu/~sandholm/)
## Awesome
- [Reinforcement learning resources curated](https://github.com/aikorea/awesome-rl)
- [Awesome Reinforcement Learning(RL) for Natural Language Processing(NLP))](https://github.com/adityathakker/awesome-rl-nlp)
- [Paper list of multi-agent reinforcement learning (MARL) )](https://github.com/LantaoYu/MARL-Papers)
- [A list of recent papers regarding deep reinforcement learning](https://github.com/junhyukoh/deep-reinforcement-learning-papers)
- [TensorFlow implementation of Deep Reinforcement Learning papers ](https://github.com/carpedm20/deep-rl-tensorflow)
- [Deep Reinforcement Learning Papers](https://github.com/junhyukoh/deep-reinforcement-learning-papers)
- [Reinforcement learning resources curated](https://github.com/aikorea/awesome-rl)
- [This project is for learning and researching on Deep RL. Maintained by University AI researchers](https://github.com/tigerneil/aExcerpt of 10,424 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5f462a3949669f87, name:reinforcement learning, desc:reinforcement learning