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/
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://www.oreilly.com/ideas/reinforcement-learning-for-complex-goals-using-tensorflow
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:
- Part 0 — Q-Learning Agents
- Part 1 — Two-Armed Bandit
- Part 1.5 — Contextual Bandits
- Part 2 — Policy-Based Agents
- Part 3 — Model-Based RL
- Part 4 — Deep Q-Networks and Beyond
- Part 5 — Visualizing an Agent’s Thoughts and Actions
- Part 6 — Partial Observability and Deep Recurrent Q-Networks
- Part 7 — Action-Selection Strategies for Exploration
- 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://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/ai-society/my-first-experience-with-deep-reinforcement-learning-1743594f0361
http://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/
More from my Simple Reinforcement Learning with Tensorflow series:
- Part 0 — Q-Learning Agents
- Part 1 — Two-Armed Bandit
- Part 1.5 — Contextual Bandits
- Part 2 — Policy-Based Agents
- Part 3 — Model-Based RL
- Part 4 — Deep Q-Networks and Beyond
- Part 5 — Visualizing an Agent’s Thoughts and Actions
- Part 6 — Partial Observability and Deep Recurrent Q-Networks
- Part 7 — Action-Selection Strategies for Exploration
- 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)