Reference for Reinforcement Learning

Papers

RL for game playing

Newest (in recent 2 years):
  1. Heinrich, Johannes, and David Silver. “Deep Reinforcement Learning from Self-Play in Imperfect-Information Games” (2016).
  2. Finn, Chelsea, Tianhe Yu, Justin Fu, Pieter Abbeel, and Sergey Levine. “Generalizing Skills with Semi-Supervised Reinforcement Learning.” arXiv (2016)
Classic:
  1. Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. “Playing Atari with Deep Reinforcement Learning” (2013).
  2. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A., Veness, J., Bellemare, M., Graves, A., Riedmiller, M., Fidjeland, A., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D. (2015) Human-level control through deep reinforcement learning, Cah Rev The, nature 518, 529–533.
  3. Nair, Arun, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro Maria, Vedavyas Panneershelvam, et al. “Massively Parallel Methods for Deep Reinforcement Learning.” arXiv(2015).

Autonomous Driving

  1. Fridman, Lex, and Bryan Reimer. “Semi-Automated Annotation of Discrete States in Large Video Datasets.” arXiv (2016).

Software Frameworks

  1. Neubig, Graham, Chris Dyer, Yoav Goldberg, Austin Matthews, Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, et al. “DyNet: The Dynamic Neural Network Toolkit” (2017).

Hareware

  1. Sze, Vivienne, Yu-Hsin Chen, Joel Emer, Amr Suleiman, and Zhengdong Zhang. “Hardware for Machine Learning: Challenges and Opportunities.” arXiv (2016).

Researchers

  1. Dr. John Schulman

GitHub Projects

  1. gym

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