「ML」 强化学习资料汇总

智能算法在没有人为指导的情况下，通过不断的试错来提升任务性能的过程；

reinforcement learning

1 综述

1. A Survey of Deep Reinforcement Learning in Video Games
   2019-12-23 paper

2. A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions
   2020-01-19 paper

2 理论

1. Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
   ICML 2019 2018-12-02 paper

2. Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
   ICML 2019 2019-05-21 paper

3 其他

1. Meta-Gradient Reinforcement Learning
   2018-05-24 Paper

2. A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems

3. Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications
   2018-12-31 Paper

4. Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning
   2019-04-16 Paper
   多代理合作的触摸屏 3D 导航协议；

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TOP

附录

A 研究员

1. David Silver
2. Richard S. Sutton
3. David Abel

B 参考资料

1 书籍

1. Richard S. Sutton, Andrew G. Barto. Reinforcement Learning:An Introduction[M].
   主页 | python 实现 | stanford-pdf

2 课程

1. An Introduction to Reinforcement Learning
   David Silver， Video-youtube | Video-Bilibili | lectures | python 实现 | google-drive-包括讲义和视频

2. OpenAI-spinningup

3 博客

1. 莫凡. 强化学习[EB/OL]. https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/. 2016-11-03/2019-04-19.
2. 不会停的蜗牛. 一文了解强化学习[EB/OL]. https://www.jianshu.com/p/f4409a8b7f71. 2017-06-16/2019-04-19.
3. JustFollowUs. Reinforcement-Learning[EB/OL]. https://github.com/JustFollowUs/Reinforcement-Learning. 2017-04-11/2019-04-19.
4. 王小惟. 强化学习从入门到放弃的资料[EB/OL]. https://zhuanlan.zhihu.com/p/34918639. 2018-03-25/2019-04-22.
5. ICLR 2018 总结

4 报告

RLDM 2019 笔记-David Abel