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Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent settings.
Markov games as a framework for multi-agent reinforcement learning
Michael L Littman · 1994
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The dynamics of reinforcement learning in cooperative multiagent systems
Caroline Claus and Craig Boutilier · 1998
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Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
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A concise introduction to decentralized POMDPs
Frans A Oliehoek, Christopher Amato, et al · 2016
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2016
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Learning with opponent-learning awareness
Jakob N Foerster, Richard Y Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, and Igor Mordatch · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch · 2017
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Emergence of grounded compositional language in multi-agent populations
Igor Mordatch and Pieter Abbeel · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, et al · 2018
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Counterfactual multi-agent policy gradients
Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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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
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QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
Cited alongside, same era.
Value-decomposition networks for cooperative multi-agent learning based on team reward
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al · 2018
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The mirage of action-dependent baselines in reinforcement learning
George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard Turner, Zoubin Ghahramani, and Sergey Levine · 2018
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Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyslaw Debiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Christopher Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique Pondé de Oliveira Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, and Susan Zhang · 2019
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A closer look at deep policy gradients
Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Madry · 2020
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Ai-qmix: Attention and imagination for dynamic multi-agent reinforcement learning
Shariq Iqbal, Christian A. Schröder de Witt, Bei Peng, Wendelin Böhmer, Shimon Whiteson, and Fei Sha · 2020
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Google research football: A novel reinforcement learning environment
Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, and Sylvain Gelly · 2020
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Weighted qmix: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Gregory Farquhar, Bei Peng, and Shimon Whiteson · 2020
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What matters for on-policy deep actor-critic methods? a large-scale study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Leonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, and Olivier Bachem · 2021
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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 · 2019
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Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, M Wojciech Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, H David Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, P John Agapiou, Max Jaderberg, S Alexander Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, L Tom Paine, Caglar Gulcehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, and David Silver · 2019
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Emergent tool use from multi-agent autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor M. Markov, Yi Wu, Glenn Powell, Bob McGrew, and Igor Mordatch · 2020
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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, et al · 2020
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Is independent learning all you need in the starcraft multi-agent challenge?
Christian Schroeder de Witt, Tarun Gupta, Denys Makoviichuk, Viktor Makoviychuk, Philip H. S. Torr, Mingfei Sun, and Shimon Whiteson · 2020
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Implementation matters in deep rl: A case study on ppo and trpo
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Madry · 2020
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Simplified action decoder for deep multi-agent reinforcement learning
Hengyuan Hu and Jakob N Foerster · 2020
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