Deep Reinforcement Learning

Reinforcement-Learning-Introduction-Adaptive-Computation

http://incompleteideas.net/book/bookdraft2017nov5.pdf

http://incompleteideas.net/book/ebook/the-book.html

https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation/dp/0262193981

https://orbi.ulg.ac.be/bitstream/2268/27963/1/book-FA-RL-DP.pdf

http://videolectures.net/deeplearning2017_montreal/

http://www.clipconverter.cc/

Reinforcement Learning--David Silver

http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching.html

https://www.youtube.com/watch?v=2pWv7GOvuf0

COMBINING POLICY GRADIENT AND Q-LEARNING

https://arxiv.org/pdf/1611.01626.pdf

https://www.quora.com/Whats-the-difference-between-reinforcement-Learning-and-Deep-learning

https://stats.stackexchange.com/questions/144154/supervised-learning-unsupervised-learning-and-reinforcement-learning-workflow

https://www.quora.com/What-is-the-difference-between-supervised-unsupervised-reinforcement-and-deep-learning

https://www.quora.com/Is-reinforcement-learning-the-combination-of-unsupervised-learning-and-supervised-learning

https://www.quora.com/What-is-the-difference-between-supervised-unsupervised-reinforcement-and-deep-learning

https://www.oreilly.com/ideas/reinforcement-learning-for-complex-goals-using-tensorflow

https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-6-partial-observability-and-deep-recurrent-q-68463e9aeefc

https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0

最前沿：深度学习训练方法大革新，反向传播训练不再唯一

https://zhuanlan.zhihu.com/p/22143664

最前沿：让计算机学会学习Let Computers Learn to Learn

https://zhuanlan.zhihu.com/p/21362413?refer=intelligentunit

深度增强学习之Policy Gradient方法1

https://zhuanlan.zhihu.com/p/21725498

https://deepmind.com/blog/#decoupled-neural-interfaces-using-synthetic-gradients

ore from my Simple Reinforcement Learning with Tensorflow series:

1. Part 0 — Q-Learning Agents
2. Part 1 — Two-Armed Bandit
3. Part 1.5 — Contextual Bandits
4. Part 2 — Policy-Based Agents
5. Part 3 — Model-Based RL
6. Part 4 — Deep Q-Networks and Beyond
7. Part 5 — Visualizing an Agent’s Thoughts and Actions
8. Part 6 — Partial Observability and Deep Recurrent Q-Networks
9. Part 7 — Action-Selection Strategies for Exploration
10. Part 8 — Asynchronous Actor-Critic Agents (A3C)

https://keon.io/deep-q-learning/

Human-level control through deep reinforcement learning

https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf

http://rll.berkeley.edu/deeprlcourse/

https://bcourses.berkeley.edu/courses/1453965/pages/cs294-129-designing-visualizing-and-understanding-deep-neural-networks

https://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html

如何用简单例子讲解 Q - learning 的具体过程？

https://www.zhihu.com/question/26408259

https://deeplearning4j.org/reinforcementlearning.html

https://deeplearning4j.org/neuralnet-overview.html

https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-reinforcement-learning/

https://medium.com/beyond-intelligence/reinforcement-learning-or-evolutionary-strategies-nature-has-a-solution-both-8bc80db539b3

https://medium.com/ai-society/my-first-experience-with-deep-reinforcement-learning-1743594f0361

https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0

http://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/

More from my Simple Reinforcement Learning with Tensorflow series:

1. Part 0 — Q-Learning Agents
2. Part 1 — Two-Armed Bandit
3. Part 1.5 — Contextual Bandits
4. Part 2 — Policy-Based Agents
5. Part 3 — Model-Based RL
6. Part 4 — Deep Q-Networks and Beyond
7. Part 5 — Visualizing an Agent’s Thoughts and Actions
8. Part 6 — Partial Observability and Deep Recurrent Q-Networks
9. Part 7 — Action-Selection Strategies for Exploration
10. Part 8 — Asynchronous Actor-Critic Agents (A3C)

Deep Reinforcement Learning 深度增强学习资源 (持续更新）

https://zhuanlan.zhihu.com/p/20885568

深度解读AlphaGo

https://zhuanlan.zhihu.com/p/20893777

深度学习论文阅读路线图 Deep Learning Papers Reading Roadmap

https://zhuanlan.zhihu.com/p/23080129

ICLR 2017 DRL相关论文

https://zhuanlan.zhihu.com/p/23807875

https://www.intelnervana.com/demystifying-deep-reinforcement-learning/

http://www.jmlr.org/papers/volume6/murphy05a/murphy05a.pdf

https://deepmind.com/research/publications/

https://deepmind.com/blog/alphago-zero-learning-scratch/

Mastering the Game of Go without Human Knowledge

https://www.nature.com/articles/doi:10.1038/nature24270

https://en.wikipedia.org/wiki/State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action

DQN 从入门到放弃1 DQN与增强学习

https://zhuanlan.zhihu.com/p/21262246?refer=intelligentunit

DQN 从入门到放弃4 动态规划与Q-Learning

https://zhuanlan.zhihu.com/p/21378532?refer=intelligentunit

DQN从入门到放弃5 深度解读DQN算法

https://zhuanlan.zhihu.com/p/21421729

强化学习系列之九:Deep Q Network (DQN)

http://www.algorithmdog.com/drl