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 58 articles )

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  1. Antonio Serrano-Muñoz, Nestor Arana-Arexolaleiba, Dimitrios Chrysostomou, Simon Bøgh: skrl: Modular and Flexible Library for Reinforcement Learning (2022) arXiv
  2. Benatti, Simone; Young, Aaron; Elmquist, Asher; Taves, Jay; Tasora, Alessandro; Serban, Radu; Negrut, Dan: End-to-end learning for off-road terrain navigation using the chrono open-source simulation platform (2022)
  3. Brammer, Janis; Lutz, Bernhard; Neumann, Dirk: Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning (2022)
  4. Conor Heins; Beren Millidge; Daphne Demekas; Brennan Klein; Karl Friston; Iain D. Couzin; Alexander Tschantz: pymdp: A Python library for active inference in discrete state spaces (2022) not zbMATH
  5. Cowen-Rivers, Alexander I.; Palenicek, Daniel; Moens, Vincent; Abdullah, Mohammed Amin; Sootla, Aivar; Wang, Jun; Bou-Ammar, Haitham: SAMBA: safe model-based & active reinforcement learning (2022)
  6. Kilinc, Ozsel; Montana, Giovanni: Reinforcement learning for robotic manipulation using simulated locomotion demonstrations (2022)
  7. Zhai, Yuexiang; Baek, Christina; Zhou, Zhengyuan; Jiao, Jiantao; Ma, Yi: Computational benefits of intermediate rewards for goal-reaching policy learning (2022)
  8. Zhao, Ying; Li, Liang; Lanteri, Stéphane; Viquerat, Jonathan: Dynamic metasurface control using deep reinforcement learning (2022)
  9. Aebel Joe Shibu, Sadhana S, Shilpa N, Pratyush Kumar: VeRLPy: Python Library for Verification of Digital Designs with Reinforcement Learning (2021) arXiv
  10. Akrour, Riad; Atamna, Asma; Peters, Jan: Convex optimization with an interpolation-based projection and its application to deep learning (2021)
  11. Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi: Ecole: A Library for Learning Inside MILP Solvers (2021) arXiv
  12. Colas, Cédric; Hejblum, Boris; Rouillon, Sebastien; Thiébaut, Rodolphe; Oudeyer, Pierre-Yves; Moulin-Frier, Clément; Prague, Mélanie: EpidemiOptim: a toolbox for the optimization of control policies in epidemiological models (2021)
  13. D’eramo, Carlo; Tateo, Davide; Bonarini, Andrea; Restelli, Marcello; Peters, Jan: MushroomRL: simplifying reinforcement learning research (2021)
  14. Erdem Bıyık, Aditi Talati, Dorsa Sadigh: APReL: A Library for Active Preference-based Reward Learning Algorithms (2021) arXiv
  15. Garnier, Paul; Viquerat, Jonathan; Rabault, Jean; Larcher, Aurélien; Kuhnle, Alexander; Hachem, Elie: A review on deep reinforcement learning for fluid mechanics (2021)
  16. Hachem, E.; Ghraieb, H.; Viquerat, J.; Larcher, A.; Meliga, P.: Deep reinforcement learning for the control of conjugate heat transfer (2021)
  17. Han, Minghao; Tian, Yuan; Zhang, Lixian; Wang, Jun; Pan, Wei: Reinforcement learning control of constrained dynamic systems with uniformly ultimate boundedness stability guarantee (2021)
  18. Hanna, Josiah P.; Niekum, Scott; Stone, Peter: Importance sampling in reinforcement learning with an estimated behavior policy (2021)
  19. Hu, Yazhou; Tang, Fengzhen; Chen, Jun; Wang, Wenxue: Quantum-enhanced reinforcement learning for control: a preliminary study (2021)
  20. Kimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel: SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning (2021) arXiv

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