OpenAI Gym

OpenAI Gym. OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can share their results and compare the performance of algorithms. This whitepaper discusses the components of OpenAI Gym and the design decisions that went into the software.

References in zbMATH (referenced in 21 articles )

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  1. Bard, Nolan; Foerster, Jakob N.; Chandar, Sarath; Burch, Neil; Lanctot, Marc; Song, H. Francis; Parisotto, Emilio; Dumoulin, Vincent; Moitra, Subhodeep; Hughes, Edward; Dunning, Iain; Mourad, Shibl; Larochelle, Hugo; Bellemare, Marc G.; Bowling, Michael: The Hanabi challenge: a new frontier for AI research (2020)
  2. Christian D. Hubbs, Hector D. Perez, Owais Sarwar, Nikolaos V. Sahinidis, Ignacio E. Grossmann, John M. Wassick: OR-Gym: A Reinforcement Learning Library for Operations Research Problem (2020) arXiv
  3. Millidge, Beren: Deep active inference as variational policy gradients (2020)
  4. Ruehle, Fabian: Data science applications to string theory (2020)
  5. Stefan Heid; Daniel Weber; Henrik Bode; Eyke Hüllermeier; Oliver Wallscheid: OMG: A Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control (2020) not zbMATH
  6. Xiao-Yang Liu, Hongyang Yang, Qian Chen, Runjia Zhang, Liuqing Yang, Bowen Xiao, Christina Dan Wang: FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance (2020) arXiv
  7. Zihan Ding, Tianyang Yu, Yanhua Huang, Hongming Zhang, Luo Mai, Hao Dong: RLzoo: A Comprehensive and Adaptive Reinforcement Learning Library (2020) arXiv
  8. Bilinski, Mark; Ferguson-Walter, Kimberly; Fugate, Sunny; Gabrys, Ryan; Mauger, Justin; Souza, Brian: You only lie twice: a multi-round cyber deception game of questionable veracity (2019)
  9. Halverson, James; Nelson, Brent; Ruehle, Fabian: Branes with brains: exploring string vacua with deep reinforcement learning (2019)
  10. Parisi, Simone; Tangkaratt, Voot; Peters, Jan; Khan, Mohammad Emtiyaz: TD-regularized actor-critic methods (2019)
  11. Sergey Kolesnikov, Oleksii Hrinchuk: Catalyst.RL: A Distributed Framework for Reproducible RL Research (2019) arXiv
  12. Tristan Deleu, Tobias Würfl, Mandana Samiei, Joseph Paul Cohen, Yoshua Bengio: Torchmeta: A Meta-Learning library for PyTorch (2019) arXiv
  13. Yasuhiro Fujita, Toshiki Kataoka, Prabhat Nagarajan, Takahiro Ishikawa: ChainerRL: A Deep Reinforcement Learning Library (2019) arXiv
  14. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
  15. Aqeel Labash; Ardi Tampuu; Tambet Matiisen; Jaan Aru; Raul Vicente: APES: a Python toolbox for simulating reinforcement learning environments (2018) arXiv
  16. Hananel Hazan, Daniel J. Saunders, Hassaan Khan, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma: BindsNET: A machine learning-oriented spiking neural networks library in Python (2018) arXiv
  17. Michael Schaarschmidt, Sven Mika, Kai Fricke, Eiko Yoneki: RLgraph: Modular Computation Graphs for Deep Reinforcement Learning (2018) arXiv
  18. Ueltzhöffer, Kai: Deep active inference (2018)
  19. Zhiting Hu; Haoran Shi; Zichao Yang; Bowen Tan; Tiancheng Zhao; Junxian He; Wentao Wang; Xingjiang Yu; Lianhui Qin; Di Wang; Xuezhe Ma; Hector Liu; Xiaodan Liang; Wanrong Zhu; Devendra Singh Sachan; Eric P. Xing: Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation (2018) arXiv
  20. Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica: RLlib: Abstractions for Distributed Reinforcement Learning (2017) arXiv

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