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 15 articles )

Showing results 1 to 15 of 15.
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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. Ruehle, Fabian: Data science applications to string theory (2020)
  4. Halverson, James; Nelson, Brent; Ruehle, Fabian: Branes with brains: exploring string vacua with deep reinforcement learning (2019)
  5. Parisi, Simone; Tangkaratt, Voot; Peters, Jan; Khan, Mohammad Emtiyaz: TD-regularized actor-critic methods (2019)
  6. Sergey Kolesnikov, Oleksii Hrinchuk: Catalyst.RL: A Distributed Framework for Reproducible RL Research (2019) arXiv
  7. Tristan Deleu, Tobias Würfl, Mandana Samiei, Joseph Paul Cohen, Yoshua Bengio: Torchmeta: A Meta-Learning library for PyTorch (2019) arXiv
  8. Yasuhiro Fujita, Toshiki Kataoka, Prabhat Nagarajan, Takahiro Ishikawa: ChainerRL: A Deep Reinforcement Learning Library (2019) arXiv
  9. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
  10. Aqeel Labash; Ardi Tampuu; Tambet Matiisen; Jaan Aru; Raul Vicente: APES: a Python toolbox for simulating reinforcement learning environments (2018) arXiv
  11. 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
  12. Michael Schaarschmidt, Sven Mika, Kai Fricke, Eiko Yoneki: RLgraph: Modular Computation Graphs for Deep Reinforcement Learning (2018) arXiv
  13. Ueltzhöffer, Kai: Deep active inference (2018)
  14. 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
  15. 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