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 26 articles )

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  1. Paris, Romain; Beneddine, Samir; Dandois, Julien: Robust flow control and optimal sensor placement using deep reinforcement learning (2021)
  2. Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L. Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido van de Ven, Martin Mundt, Qi She, Keiland Cooper, Jeremy Forest, Eden Belouadah, Simone Calderara, German I. Parisi, Fabio Cuzzolin, Andreas Tolias, Simone Scardapane, Luca Antiga, Subutai Amhad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu, Davide Maltoni: Avalanche: an End-to-End Library for Continual Learning (2021) arXiv
  3. 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)
  4. Chen, Si-An; Tangkaratt, Voot; Lin, Hsuan-Tien; Sugiyama, Masashi: Active deep Q-learning with demonstration (2020)
  5. 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
  6. Gonzague Henri, Tanguy Levent, Avishai Halev, Reda Alami, Philippe Cordier: pymgrid: An Open-Source Python Microgrid Simulator for Applied Artificial Intelligence Research (2020) arXiv
  7. Millidge, Beren: Deep active inference as variational policy gradients (2020)
  8. Ruehle, Fabian: Data science applications to string theory (2020)
  9. 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
  10. Thalmeier, Dominik; Kappen, Hilbert J.; Totaro, Simone; Gómez, Vicenç: Adaptive smoothing for path integral control (2020)
  11. 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
  12. Zihan Ding, Tianyang Yu, Yanhua Huang, Hongming Zhang, Luo Mai, Hao Dong: RLzoo: A Comprehensive and Adaptive Reinforcement Learning Library (2020) arXiv
  13. 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)
  14. Halverson, James; Nelson, Brent; Ruehle, Fabian: Branes with brains: exploring string vacua with deep reinforcement learning (2019)
  15. Parisi, Simone; Tangkaratt, Voot; Peters, Jan; Khan, Mohammad Emtiyaz: TD-regularized actor-critic methods (2019)
  16. Sergey Kolesnikov, Oleksii Hrinchuk: Catalyst.RL: A Distributed Framework for Reproducible RL Research (2019) arXiv
  17. Tristan Deleu, Tobias Würfl, Mandana Samiei, Joseph Paul Cohen, Yoshua Bengio: Torchmeta: A Meta-Learning library for PyTorch (2019) arXiv
  18. Yasuhiro Fujita, Toshiki Kataoka, Prabhat Nagarajan, Takahiro Ishikawa: ChainerRL: A Deep Reinforcement Learning Library (2019) arXiv
  19. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
  20. Aqeel Labash; Ardi Tampuu; Tambet Matiisen; Jaan Aru; Raul Vicente: APES: a Python toolbox for simulating reinforcement learning environments (2018) arXiv

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