A collection of resources about deep reinforcement learning
- https://www.cs.toronto.edu/%7Evmnih/docs/dqn.pdf (Play Atari game with deep reinforcement learning)
- http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html (Human level control with deep reinforcement learning)
- https://arxiv.org/pdf/1511.05952v2.pdf (Prioritized experience replay)
- https://arxiv.org/pdf/1511.06581v3.pdf (Dueling DQN)
- https://arxiv.org/abs/1509.06461 (Deep reinforcement learning with double Q Learning)
- https://arxiv.org/pdf/1603.00748v1.pdf (Deep Q learning with NAF)
- http://jmlr.org/proceedings/papers/v32/silver14.pdf (Deterministic policy gradient)
- https://arxiv.org/pdf/1509.02971v5.pdf (Continuous control with deep reinforcement learning) (DDPG)
- https://arxiv.org/abs/1602.01783 (Asynchronous Methods for Deep Reinforcement Learning) (A3C)
- https://arxiv.org/abs/1511.06295 (Policy distillation)
- https://arxiv.org/pdf/1605.09128v1.pdf (Control of Memory, Active Perception, and Action in Minecraft)
- https://arxiv.org/pdf/1606.01868v2.pdf (Unifying Count-Based Exploration and Intrinsic Motivation)
- https://arxiv.org/pdf/1507.00814v3.pdf (Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models)
- https://arxiv.org/pdf/1507.08750v2.pdf (Action-Conditional Video Prediction using Deep Networks in Atari Games)
- https://web.eecs.umich.edu/~baveja/Papers/ICML2016.pdf (Control of Memory, Active Perception, and Action in Minecraft)
- https://arxiv.org/pdf/1701.08734.pdf (PathNet)
- http://icml.cc/2016/tutorials/deep_rl_tutorial.pdf (ICML 2016, Deep Reinforcement Learning tutorial)
- https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-4-deep-q-networks-and-beyond-8438a3e2b8df#.28wv34w3a (DQN tutorial)
- http://karpathy.github.io/2016/05/31/rl/ (Play pong with deep reinforcement learning based on pixel)