https://github.com/williamd4112/awesome-deep-reinforcement-learning

A collection of resources about deep reinforcement learning
https://github.com/williamd4112/awesome-deep-reinforcement-learning

List: awesome-deep-reinforcement-learning

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A collection of resources about deep reinforcement learning

• Host: GitHub
• URL: https://github.com/williamd4112/awesome-deep-reinforcement-learning
• Owner: williamd4112
• Created: 2016-12-29T16:37:58.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2017-02-24T10:29:33.000Z (over 7 years ago)
• Last Synced: 2024-05-22T05:00:39.038Z (6 months ago)
• Size: 3.91 KB
• Stars: 25
• Watchers: 4
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# awesome-deep-reinforcement-learning

A collection of resources about deep reinforcement learning

# Papers

- 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)

# Tutorial

- 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)

# Blog

- http://karpathy.github.io/2016/05/31/rl/ (Play pong with deep reinforcement learning based on pixel)

# Repo

- https://github.com/devsisters/DQN-tensorflow (DQN)

- https://github.com/stevenpjg/ddpg-aigym (DDPG)

- https://github.com/miyosuda/async_deep_reinforce (A3C)

- https://github.com/openai/universe-starter-agent (A3C)