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Multi-agent policy gradient (MAPG) methods recently witness vigorous progress.
Neuronlike adaptive elements that can solve difficult learning control problems
A. G. Barto, R. S. Sutton, and C. W. Anderson · 1983
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Off-policy policy search
Nicolas Meuleau, Leonid Peshkin, Leslie P Kaelbling, and Kee-Eung Kim · 2000
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Eligibility traces for off-policy policy evaluation
Doina Precup, Richard S Sutton, and Satinder Singh · 2000
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Learning sequences of actions in collectives of autonomous agents
Kagan Tumer, Adrian K. Agogino, and David H. Wolpert · 2002
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Optimal payoff functions for members of collectives
David H Wolpert and Kagan Tumer · 2002
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Unifying temporal and structural credit assignment problems
Adrian K. Agogino and Kagan Tumer · 2004
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Collaborative multiagent reinforcement learning by payoff propagation
Jelle R Kok and Nikos Vlassis · 2006
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Qplex: Duplex dueling multi-agent q-learning
Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, and Chongjie Zhang · 2008
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On a connection between importance sampling and the likelihood ratio policy gradient
Tang Jie and Pieter Abbeel · 2010
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Coordinated multi-agent reinforcement learning in networked distributed pomdps
Chongjie Zhang and Victor Lesser · 2011
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Off-policy actor-critic
Thomas Degris, Martha White, and Richard S Sutton · 2012
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Guided policy search
Sergey Levine and Vladlen Koltun · 2013
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Ioannis Alexandros Assael, Nando de Freitas, and Shimon Whiteson · 2016
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Safe and efficient off-policy reinforcement learning
Rémi Munos, Tom Stepleton, Anna Harutyunyan, and Marc Bellemare · 2016
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A concise introduction to decentralized POMDPs , volume 1
Frans A Oliehoek, Christopher Amato, et al · 2016
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Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Rob Fergus, et al · 2016
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Sample efficient actor-critic with experience replay
Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos, Koray Kavukcuoglu, and Nando de Freitas · 2016
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Stabilising experience replay for deep multi-agent reinforcement learning
Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip HS Torr, Pushmeet Kohli, and Shimon Whiteson · 2017
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Cooperative multi-agent control using deep reinforcement learning
Jayesh K Gupta, Maxim Egorov, and Mykel Kochenderfer · 2017
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A survey of learning in multiagent environments: Dealing with non-stationarity
Pablo Hernandez-Leal, Michael Kaisers, Tim Baarslag, and Enrique Munoz de Cote · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Multi-agent cooperation and the emergence of (natural) language
Angeliki Lazaridou, Alexander Peysakhovich, and Marco Baroni · 2017
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Mean field multi-agent reinforcement learning
Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, and Jun Wang · 2018
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Fully decentralized multi-agent reinforcement learning with networked agents
Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, and Tamer Basar · 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, Chris Hesse, et al · 2019
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The representational capacity of action-value networks for multi-agent reinforcement learning
Jacopo Castellini, Frans A Oliehoek, Rahul Savani, and Shimon Whiteson · 2019
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
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Diff-dac: Distributed actor-critic for multitask deep reinforcement learning
Sergio Valcarcel Macua, Aleksi Tukiainen, Daniel García-Ocaña Hernández, David Baldazo, Enrique Munoz de Cote, and Santiago Zazo · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2017
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Deep decentralized multi-task multi-agent reinforcement learning under partial observability
Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P How, and John Vian · 2017
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Distral: Robust multitask reinforcement learning
Yee Teh, Victor Bapst, Wojciech M Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017
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Multi-agent fully decentralized value function learning with linear convergence rates
Lucas Cassano, Kun Yuan, and Ali H Sayed · 2018
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Model-based value estimation for efficient model-free reinforcement learning
Vladimir Feinberg, Alvin Wan, Ion Stoica, Michael I Jordan, Joseph E Gonzalez, and Sergey Levine · 2018
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Tarmac: Targeted multi-agent communication
Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, and Joelle Pineau · 2019
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Actor-attention-critic for multi-agent reinforcement learning
Shariq Iqbal and Fei Sha · 2019
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Human-level performance in 3d multiplayer games with population-based reinforcement learning
Max Jaderberg, Wojciech M Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castaneda, Charles Beattie, Neil C Rabinowitz, Ari S Morcos, Avraham Ruderman, et al · 2019
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Social influence as intrinsic motivation for multi-agent deep reinforcement learning
Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro Ortega, Dj Strouse, Joel Z Leibo, and Nando De Freitas · 2019
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The starcraft multi-agent challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim GJ Rudner, Chia-Man Hung, Philip HS Torr, Jakob 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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A multi-agent off-policy actor-critic algorithm for distributed reinforcement learning
Wesley Suttle, Zhuoran Yang, Kaiqing Zhang, Zhaoran Wang, Tamer Basar, and Ji Liu · 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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Probabilistic recursive reasoning for multi-agent reinforcement learning
Ying Wen, Yaodong Yang, Rui Luo, Jun Wang, and Wei Pan · 2019
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Multi-agent reinforcement learning: A selective overview of theories and algorithms
Kaiqing Zhang, Zhuoran Yang, and Tamer Başar · 2019
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Distributed off-policy actor-critic reinforcement learning with policy consensus
Yan Zhang and Michael M Zavlanos · 2019
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Emergent tool use from multi-agent autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, and Igor Mordatch · 2020
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Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder De Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2020
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