Fetching the paper…
Reading the bibliography…
A major challenge of multiagent reinforcement learning (MARL) is the curse of multiagents, where the size of the joint action space scales exponentially with the number of agents.
Stochastic games
Lloyd S Shapley · 1953
Earlier work this paper cites.
The complexity of two-person zero-sum games in extensive form
Daphne Koller and Nimrod Megiddo · 1992
Earlier work this paper cites.
Markov games as a framework for multi-agent reinforcement learning
Michael L Littman · 1994
Earlier work this paper cites.
A course in game theory
Martin J Osborne and Ariel Rubinstein · 1994
Earlier work this paper cites.
A simple adaptive procedure leading to correlated equilibrium
Sergiu Hart and Andreu Mas-Colell · 2000
Earlier work this paper cites.
Friend-or-foe q-learning in general-sum games
Michael L Littman · 2001
Earlier work this paper cites.
Nash q-learning for general-sum stochastic games
Junling Hu and Michael P Wellman · 2003
Earlier work this paper cites.
Prediction, learning, and games
Nicolo Cesa-Bianchi and Gábor Lugosi · 2006
Earlier work this paper cites.
Finding equilibria in large sequential games of imperfect information
Andrew Gilpin and Tuomas Sandholm · 2006
Earlier work this paper cites.
PAC model-free reinforcement learning
Alexander L Strehl, Lihong Li, Eric Wiewiora, John Langford, and Michael L Littman · 2006
Earlier work this paper cites.
From external to internal regret
Avrim Blum and Yishay Mansour · 2007
Earlier work this paper cites.
Regret minimization in games with incomplete information
Martin Zinkevich, Michael Johanson, Michael Bowling, and Carmelo Piccione · 2007
Earlier work this paper cites.
Near-optimal regret bounds for reinforcement learning
Thomas Jaksch, Ronald Ortner, and Peter Auer · 2010
Earlier work this paper cites.
Swarm robotics: a review from the swarm engineering perspective
Manuele Brambilla, Eliseo Ferrante, Mauro Birattari, and Marco Dorigo · 2013
Earlier work this paper cites.
On the complexity of approximating a nash equilibrium
Constantinos Daskalakis · 2013
Earlier work this paper cites.
Strategy iteration is strongly polynomial for 2-player turn-based stochastic games with a constant discount factor
Thomas Dueholm Hansen, Peter Bro Miltersen, and Uri Zwick · 2013
Earlier work this paper cites.
Generalization and exploration via randomized value functions
Ian Osband, Benjamin Van Roy, and Zheng Wen · 2014
Earlier work this paper cites.
Explore no more: Improved high-probability regret bounds for non-stochastic bandits
Gergely Neu · 2015
Earlier work this paper cites.
On lower bounds for regret in reinforcement learning
Ian Osband and Benjamin Van Roy · 2016
Earlier work this paper cites.
Safe, multi-agent, reinforcement learning for autonomous driving
Shai Shalev-Shwartz, Shaked Shammah, and Amnon Shashua · 2016
Cited alongside, same era.
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.
Minimax regret bounds for reinforcement learning
Mohammad Gheshlaghi Azar, Ian Osband, and Rémi Munos · 2017
Cited alongside, same era.
Unifying pac and regret: Uniform pac bounds for episodic reinforcement learning
Christoph Dann, Tor Lattimore, and Emma Brunskill · 2017
Cited alongside, same era.
Contextual decision processes with low bellman rank are pac-learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2017
Cited alongside, same era.
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
Later among the works it cites.
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
Later among the works it cites.
Provable self-play algorithms for competitive reinforcement learning
Yu Bai and Chi Jin · 2020
Later among the works it cites.
Near-optimal reinforcement learning with self-play
Yu Bai, Chi Jin, and Tiancheng Yu · 2020
Later among the works it cites.
Emergent tool use from multi-agent autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, and Igor Mordatch · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch · 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.
Value-decomposition networks for cooperative multi-agent learning
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al · 2017
Cited alongside, same era.
Online reinforcement learning in stochastic games
Chen-Yu Wei, Yi-Te Hong, and Chi-Jen Lu · 2017
Cited alongside, same era.
Superhuman ai for heads-up no-limit poker: Libratus beats top professionals
Noam Brown and Tuomas Sandholm · 2018
Cited alongside, same era.
Is Q-learning provably efficient?
Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, and Michael I Jordan · 2018
Cited alongside, same era.
Bandit algorithms
Tor Lattimore and Csaba Szepesvári · 2018
Cited alongside, same era.
No-regret learning dynamics for extensive-form correlated equilibrium
Andrea Celli, Alberto Marchesi, Gabriele Farina, and Nicola Gatti · 2020
Later among the works it cites.
Provably efficient reinforcement learning with linear function approximation
Chi Jin, Zhuoran Yang, Zhaoran Wang, and Michael I Jordan · 2020
Later among the works it cites.
Solving discounted stochastic two-player games with near-optimal time and sample complexity
Aaron Sidford, Mengdi Wang, Lin Yang, and Yinyu Ye · 2020
Later among the works it cites.
Linear last-iterate convergence in constrained saddle-point optimization
Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, and Haipeng Luo · 2020
Later among the works it cites.
Qiaomin Xie, Yudong Chen, Zhaoran Wang, and Zhuoran Yang · 2020
Later among the works it cites.
Learning near optimal policies with low inherent bellman error
Andrea Zanette, Alessandro Lazaric, Mykel Kochenderfer, and Emma Brunskill · 2020
Later among the works it cites.
Model-based multi-agent rl in zero-sum markov games with near-optimal sample complexity
Kaiqing Zhang, Sham M Kakade, Tamer Başar, and Lin F Yang · 2020
Later among the works it cites.
Independent policy gradient methods for competitive reinforcement learning
Constantinos Daskalakis, Dylan J Foster, and Noah Golowich · 2021
Closest in time.
A sharp analysis of model-based reinforcement learning with self-play
Qinghua Liu, Tiancheng Yu, Yu Bai, and Chi Jin · 2021
Closest in time.
Provably efficient reinforcement learning in decentralized general-sum markov games
Weichao Mao and Tamer Başar · 2021
Closest in time.
Decentralized q-learning in zero-sum markov games
Muhammed O Sayin, Kaiqing Zhang, David S Leslie, Tamer Basar, and Asuman Ozdaglar · 2021
Closest in time.
When can we learn general-sum markov games with a large number of players sample-efficiently?
Ziang Song, Song Mei, and Yu Bai · 2021
Closest in time.
Online learning in unknown markov games
Yi Tian, Yuanhao Wang, Tiancheng Yu, and Suvrit Sra · 2021
Closest in time.
Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, and Haipeng Luo · 2021
Closest in time.