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This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle ("AEC") games model.
The hanabi challenge: A new frontier for AI research
Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, and Michael Bowling · 1902
Earlier work this paper cites.
Emergent coordination through competition
Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, and Thore Graepel · 1902
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The starcraft multi-agent challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schröder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob N. Foerster, and Shimon Whiteson · 1902
Earlier work this paper cites.
Openspiel: A framework for reinforcement learning in games
Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau, Vinícius Flores Zambaldi, Satyaki Upadhyay, Julien Pérolat, Sriram Srinivasan, Finbarr Timbers, Karl Tuyls, Shayegan Omidshafiei, Daniel Hennes, Dustin Morrill, Paul Muller, Timo Ewalds, Ryan Faulkner, János Kramár, Bart De Vylder, Brennan Saeta, James Bradbury, David Ding, Sebastian Borgeaud, Matthew Lai, Julian Schrittwieser, Thomas W. Anthony, Edward Hughes, Ivo Danihelka, and Jonah Ryan-Davis · 1908
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Stochastic games
L. S. Shapley · 1953
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A course in game theory
Martin J Osborne and Ariel Rubinstein · 1994
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Temporal difference learning and td-gammon
Gerald Tesauro · 1995
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Planning, learning and coordination in multiagent decision processes
Craig Boutilier · 1996
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The complexity of decentralized control of markov decision processes
Daniel S. Bernstein, Robert Givan, Neil Immerman, and Shlomo Zilberstein · 2002
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Probabilistic pursuit-evasion games: theory, implementation, and experimental evaluation
Rene Vidal, Omid Shakernia, H Jin Kim, David Hyunchul Shim, and Shankar Sastry · 2002
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Revisiting parameter sharing in multi-agent deep reinforcement learning
J K Terry, Nathaniel Grammel, Ananth Hari, Luis Santos, and Benjamin Black · 2005
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Revisiting parameter sharing in multi-agent deep reinforcement learning
J K Terry, Nathaniel Grammel, Ananth Hari, Luis Santos, and Benjamin Black · 2005
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Supersuit: Simple microwrappers for reinforcement learning environments
J K Terry, Benjamin Black, and Ananth Hari · 2008
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Supersuit: Simple microwrappers for reinforcement learning environments
J K Terry, Benjamin Black, and Ananth Hari · 2008
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Human-level control through deep reinforcement learning
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 · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Cited alongside, same era.
Cooperative multi-agent control using deep reinforcement learning
Jayesh K Gupta, Maxim Egorov, and Mykel Kochenderfer · 2017
Cited alongside, same era.
Multi-agent reinforcement learning in sequential social dilemmas
Joel Z Leibo, Vinicius Zambaldi, Marc Lanctot, Janusz Marecki, and Thore Graepel · 2017
Cited alongside, same era.
Rllib: Abstractions for distributed reinforcement learning
Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E Gonzalez, Michael I Jordan, and Ion Stoica · 2017
Cited alongside, same era.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch · 2017
Leveraging procedural generation to benchmark reinforcement learning
Karl Cobbe, Christopher Hesse, Jacob Hilton, and John Schulman · 2019
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Stable baselines3
Antonin Raffin, Ashley Hill, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, and Noah Dormann · 2019
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An open source implementation of sequential social dilemma games
Eugene Vinitsky, Natasha Jaques, Joel Leibo, Antonio Castenada, and Edward Hughes · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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Rlcard: A toolkit for reinforcement learning in card games
Daochen Zha, Kwei-Herng Lai, Yuanpu Cao, Songyi Huang, Ruzhe Wei, Junyu Guo, and Xia Hu · 2019
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Cited alongside, same era.
Emergence of grounded compositional language in multi-agent populations
Igor Mordatch and Pieter Abbeel · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Cited alongside, same era.
Magent: A many-agent reinforcement learning platform for artificial collective intelligence
Lianmin Zheng, Jiacheng Yang, Han Cai, Weinan Zhang, Jun Wang, and Yong Yu · 2017
Cited alongside, same era.
Stable baselines
Ashley Hill, Antonin Raffin, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, Rene Traore, Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2018
Cited alongside, same era.
Distributed prioritized experience replay
Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado Van Hasselt, and David Silver · 2018
Cited alongside, same era.
Inequity aversion improves cooperation in intertemporal social dilemmas
Edward Hughes, Joel Z Leibo, Matthew Phillips, Karl Tuyls, Edgar Dueñez-Guzman, Antonio García Castañeda, Iain Dunning, Tina Zhu, Kevin McKee, Raphael Koster, et al · 2018
Cited alongside, same era.
Slime volleyball gym environment
David Ha · 2020
Closest in time.
Cleanrl: High-quality single-file implementation of deep reinforcement learning algorithms
Shengyi Huang, Rousslan Dossa, and Chang Ye · 2020
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Ai-traineree
Dawid Laszuk · 2020
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The autonomous learning library
Chris Nota · 2020
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Independent learning approaches: Overcoming multi-agent learning pathologies in team-games
G. Palmer · 2020
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Rl baselines3 zoo
Antonin Raffin · 2020
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Multi type mean field reinforcement learning
Sriram Ganapathi Subramanian, P. Poupart, Matthew E. Taylor, and N. Hegde · 2020
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Multiplayer support for the arcade learning environment
J K Terry and Benjamin Black · 2020
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Tianshou
Jiayi Weng, Minghao Zhang, Alexis Duburcq, Kaichao You, Dong Yan, Hang Su, and Jun Zhu · 2020
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openai/gym dependents, 2021
GitHub · 2021
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