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Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms.
Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 1994
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Benchmarking multi-agent deep reinforcement learning algorithms in cooperative tasks
Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, and Stefano V. Albrecht · 2006
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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MuJoCo: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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The Arcade Learning Environment: An Evaluation Platform for General Agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
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DeepMind Lab, 2016
Charles Beattie, Joel Z. Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, Julian Schrittwieser, Keith Anderson, Sarah York, Max Cant, Adam Cain, Adrian Bolton, Stephen Gaffney, Helen King, Demis Hassabis, Shane Legg, and Stig Petersen · 2016
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OpenAI Gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Asynchronous Methods for Deep Reinforcement Learning, 2016
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi I Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
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Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
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 · 2018
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Attention, learn to solve routing problems!, 2018
Wouter Kool, Herke van Hoof, and Max Welling · 2018
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gym-sokoban
Max-Philipp B. Schrader · 2018
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Imagination-augmented agents for deep reinforcement learning, 2018
Théophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, Demis Hassabis, David Silver, and Daan Wierstra · 2018
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ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems, 2019
Bharathan Balaji, Jordan Bell-Masterson, Enes Bilgin, Andreas Damianou, Pablo Moreno Garcia, Arpit Jain, Runfei Luo, Alvaro Maggiar, Balakrishnan Narayanaswamy, and Chun Ye · 2019
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Evolving diverse tsp instances by means of novel and creative mutation operators
Jakob Bossek, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann, and Heike Trautmann · 2019
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Leveraging Procedural Generation to Benchmark Reinforcement Learning
Karl Cobbe, Christopher Hesse, Jacob Hilton, and John Schulman · 2019
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GPU-Accelerated Atari Emulation for Reinforcement Learning, 2019
Behaviour suite for reinforcement learning, 2020
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 · 2020
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Machine learning for combinatorial optimization: a methodological tour d’horizon
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost · 2021
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Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation, 2021
C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, and Olivier Bachem · 2021
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Podracer architectures for scalable reinforcement learning
Matteo Hessel, Manuel Kroiss, Aidan Clark, Iurii Kemaev, John Quan, Thomas Keck, Fabio Viola, and Hado van Hasselt · 2021
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Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning, 2021
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, and Gavriel State · 2021
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Steven Dalton, Iuri Frosio, and Michael Garland · 2019
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An investigation of model-free planning, 2019
Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Théophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, and Timothy Lillicrap · 2019
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dm_env: A Python interface for reinforcement learning environments, 2019
Alistair Muldal, Yotam Doron, John Aslanides, Tim Harley, Tom Ward, and Siqi Liu · 2019
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The DeepMind JAX Ecosystem, 2020
Igor Babuschkin, Kate Baumli, Alison Bell, Surya Bhupatiraju, Jake Bruce, Peter Buchlovsky, David Budden, Trevor Cai, Aidan Clark, Ivo Danihelka, Antoine Dedieu, Claudio Fantacci, Jonathan Godwin, Chris Jones, Ross Hemsley, Tom Hennigan, Matteo Hessel, Shaobo Hou, Steven Kapturowski, Thomas Keck, Iurii Kemaev, Michael King, Markus Kunesch, Lena Martens, Hamza Merzic, Vladimir Mikulik, Tamara Norman, George Papamakarios, John Quan, Roman Ring, Francisco Ruiz, Alvaro Sanchez, Rosalia Schneider, Eren Sezener, Stephen Spencer, Srivatsan Srinivasan, Wojciech Stokowiec, Luyu Wang, Guangyao Zhou, and Fabio Viola · 2020
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Accelerating Reinforcement Learning through GPU Atari Emulation
Steven Dalton and iuri frosio · 2020
Cited alongside, same era.
OR-Gym: A Reinforcement Learning Library for Operations Research Problems, 2020
Christian D. Hubbs, Hector D. Perez, Owais Sarwar, Nikolaos V. Sahinidis, Ignacio E. Grossmann, and John M. Wassick · 2020
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Unity: A general platform for intelligent agents
Arthur Juliani, Vincent-Pierre Berges, Ervin Teng, Andrew Cohen, Jonathan Harper, Chris Elion, Chris Goy, Yuan Gao, Hunter Henry, Marwan Mattar, and Danny Lange · 2020
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Pomo: Policy optimization with multiple optima for reinforcement learning
Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, and Seungjai Min · 2020
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Open-Ended Learning Leads to Generally Capable Agents, 2021
Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michael Mathieu, Nat McAleese, Nathalie Bradley-Schmieg, Nathaniel Wong, Nicolas Porcel, Roberta Raileanu, Steph Hughes-Fitt, Valentin Dalibard, and Wojciech Marian Czarnecki · 2021
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gymnax: A JAX-based Reinforcement Learning Environment Library, 2022
Robert Tjarko Lange · 2022
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Envpool: A highly parallel reinforcement learning environment execution engine
Jiayi Weng, Min Lin, Shengyi Huang, Bo Liu, Denys Makoviichuk, Viktor Makoviychuk, Zichen Liu, Yufan Song, Ting Luo, Yukun Jiang, Zhongwen Xu, and Shuicheng Yan · 2022
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OR-Tools - Google Optimization Tools
Google · 2023
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Pgx: Hardware-accelerated parallel game simulation for reinforcement learning
Sotetsu Koyamada, Shinri Okano, Soichiro Nishimori, Yu Murata, Keigo Habara, Haruka Kita, and Shin Ishii · 2023
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Jaxmarl: Multi-agent rl environments in jax, 2023
Alexander Rutherford, Benjamin Ellis, Matteo Gallici, Jonathan Cook, Andrei Lupu, Gardar Ingvarsson, Timon Willi, Akbir Khan, Christian Schroeder de Witt, Alexandra Souly, Saptarashmi Bandyopadhyay, Mikayel Samvelyan, Minqi Jiang, Robert Tjarko Lange, Shimon Whiteson, Bruno Lacerda, Nick Hawes, Tim Rocktaschel, Chris Lu, and Jakob Nicolaus Foerster · 2023
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Investigating multi-task pretraining and generalization in reinforcement learning
Adrien Ali Taiga, Rishabh Agarwal, Jesse Farebrother, Aaron Courville, and Marc G Bellemare · 2023
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Craftax: A lightning-fast benchmark for open-ended reinforcement learning, 2024
Michael Matthews, Michael Beukman, Benjamin Ellis, Mikayel Samvelyan, Matthew Jackson, Samuel Coward, and Jakob Foerster · 2024
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