[7] Jiayi Weng, Min Lin, Shengyi Huang, Bo Liu, Denys Makoviichuk, Viktor Makoviychuk, Zichen Liu, Yufan Song, Ting Luo, Yukun Jiang, et al. EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine. In NeurIPS, 2022.
[8] Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, and Olivier Bachem. Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation. In NeurIPS Track on Datasets and Benchmarks, 2021.
[9] Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, and Gavriel State. Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning. In NeurIPS Track on Datasets and Benchmarks, 2021.
[10] James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang. JAX: composable transformations of Python+NumPy programs. https://github.com/google/jax, 2018.
[11] Justin K Terry, Benjamin Black, Mario Jayakumar, Ananth Hari, Luis Santos, Clemens Dieffendahl, Niall L Williams, Yashas Lokesh, Ryan Sullivan, Caroline Horsch, and Praveen Ravi. PettingZoo: Gym for Multi-Agent Reinforcement Learning. In NeurIPS, 2021.
[12] Gerald Tesauro. Temporal difference learning and TD-Gammon. Commun. ACM, 38(3):58-68, 1995.
[13] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. ImageNet Classification with Deep Convolutional Neural Networks. In NIPS, 2012.
[14] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529-533, 2015.
[15] Junjie Li, Sotetsu Koyamada, Qiwei Ye, Guoqing Liu, Chao Wang, Ruihan Yang, Li Zhao, Tao Qin, Tie-Yan Liu, and Hsiao-Wuen Hon. Suphx: Mastering Mahjong with Deep Reinforcement Learning. arXiv:2003.13590, 2020.
[16] M G Bellemare, Y Naddaf, J Veness, and M Bowling. The Arcade Learning Environment: An Evaluation Platform for General Agents. JAIR, 47:253-279, 2013.
[17] Daochen Zha, Kwei-Herng Lai, Songyi Huang, Yuanpu Cao, Keerthana Reddy, Juan Vargas, Alex Nguyen, Ruzhe Wei, Junyu Guo, and Xia Hu. RLCard: A Platform for Reinforcement Learning in Card Games. In IJCAI, 2020.
[18] Robert Tjarko Lange. gymnax: A JAX-based Reinforcement Learning Environment Library. http://github.com/RobertTLange/gymnax, 2022.
[19] Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, and Hado Van Hasselt. Behaviour Suite for Reinforcement Learning. In ICLR, 2020.
[20] Kenny Young and Tian Tian. MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments. arXiv:1903.03176, 2019.
[21] Clément Bonnet, Daniel Luo, Donal Byrne, Shikha Surana, Vincent Coyette, Paul Duckworth, Laurence I. Midgley, Tristan Kalloniatis, Sasha Abramowitz, Cemlyn N. Waters, Andries P. Smit, Nathan Grinsztajn, Ulrich A. Mbou Sob, Omayma Mahjoub, Elshadai Tegegn, Mohamed A. Mimouni, Raphael Boige, Ruan de Kock, Daniel Furelos-Blanco, Victor Le, Arnu Pretorius, and Alexandre Laterre. Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX. arXiv:2306.09884, 2023.
[22] Steven Dalton and Iuri Frosio. Accelerating Reinforcement Learning through GPU Atari Emulation. In NeurIPS, 2020.