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This paper list is a bit different from others. I'll put some opinion and summary on it. However, to understand the whole paper, you still have to read it by yourself!
Surely, any pull request or discussion are welcomed!

If you're a newbie in deep reinforcement learning, I suggest you to read the blog post and open course first.

• Reinforcement Learning Papers
  • Human-level control through deep reinforcement learning
  • Mastering the game of Go with deep neural networks and tree search
  • Deep Successor Reinforcement Learning
  • Action-Conditional Video Prediction using Deep Networks in Atari Games
  • Policy Distillation
  • Learning Tetris Using the Noisy Cross-Entropy Method, withcode
  • Continuous Deep Q-Learning with Model-based Acceleration
  • Value Iteration Networks
  • Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer
  • Stochastic Neural Network For Hierarchical Reinforcement Learning
  • Noisy Networks for Exploration
  • Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution
  • High-Dimensional Continuous Control Using Generalized Advantage Estimation
  • Generalizing Skills with Semi-Supervised Reinforcement Learning
  • Unsupervised Perceptual Rewards for Imitation Learning
  • Towards Deep Symbolic Reinforcement Learning
  • others
• Open Source
  • Python users
  • Lua users
• Courses
• Textbook
• Misc

👉dennybritz/reinforcement-learning
👉Daivd Silver's course about policy gradient
👉Deep Reinforcement Learning