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Similar to the role of Markov decision processes in reinforcement learning, Stochastic Games (SGs) lay the foundation for the study of multi-agent reinforcement learning (MARL) and sequential agent interactions.
Non-cooperative games
John Nash · 1951
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Stochastic games
Lloyd S Shapley · 1953
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Equilibrium in a stochastic n n -person game
Arlington M Fink · 1964
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A birth-death model of advertising and pricing
S Christian Albright and Wayne Winston · 1979
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Stochastic fishery games with myopic equilibria
M Sobel · 1982
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On the computation of fixed points in the product space of unit simplices and an application to noncooperative N person games
Gerard van der Laan and A. J. J. Talman · 1982
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A stochastic game model of football play selection
W Winston and AV Cabot · 1984
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Player aggregation in the traveling inspector model
Jerzy Filar · 1985
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Algorithms for stochastic games—a survey
TES Raghavan and Jerzy A Filar · 1991
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Markov games as a framework for multi-agent reinforcement learning
Michael L Littman · 1994
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On the complexity of the parity argument and other inefficient proofs of existence
Christos H Papadimitriou · 1994
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A generalized reinforcement-learning model: Convergence and applications
Michael L Littman and Csaba Szepesvári · 1996
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Generalized markov decision processes: Dynamic-programming and reinforcement-learning algorithms
Csaba Szepesvári and Michael L Littman · 1996
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Rational and convergent learning in stochastic games
Michael Bowling and Manuela Veloso · 2001
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Friend-or-foe q-learning in general-sum games
Michael L Littman · 2001
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Markov perfect equilibrium: I. observable actions
Eric Maskin and Jean Tirole · 2001
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R-max-a general polynomial time algorithm for near-optimal reinforcement learning
Ronen I Brafman and Moshe Tennenholtz · 2002
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Value function approximation in zero-sum markov games
Michail G Lagoudakis and Ronald Parr · 2002
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Correlated q-learning
Amy Greenwald, Keith Hall, and Roberto Serrano · 2003
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Nash q-learning for general-sum stochastic games
Junling Hu and Michael P Wellman · 2003
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Least-squares policy iteration
Michail G Lagoudakis and Ronald Parr · 2003
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Stochastic games and applications
Abraham Neyman and Sylvain Sorin · 2003
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On nash equilibria in stochastic games
Krishnendu Chatterjee, Rupak Majumdar, and Marcin Jurdziński · 2004
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Neural fitted q iteration–first experiences with a data efficient neural reinforcement learning method
Generative adversarial nets
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C Courville, and Yoshua Bengio · 2014
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Approximate dynamic programming for two-player zero-sum markov games
Julien Perolat, Bruno Scherrer, Bilal Piot, and Olivier Pietquin · 2015
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Two-timescale algorithms for learning nash equilibria in general-sum stochastic games
HL Prasad, Prashanth LA, and Shalabh Bhatnagar · 2015
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Stochastic games
Eilon Solan and Nicolas Vieille · 2015
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Softened approximate policy iteration for markov games
Julien Pérolat, Bilal Piot, Matthieu Geist, Bruno Scherrer, and Olivier Pietquin · 2016
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Martin Riedmiller · 2005
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Settling the complexity of two-player nash equilibrium
Xi Chen and Xiaotie Deng · 2006
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Cyclic equilibria in markov games
Martin Zinkevich, Amy Greenwald, and Michael Littman · 2006
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Awesome: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents
Vincent Conitzer and Tuomas Sandholm · 2007
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Approximate dynamic programming
Dimitri P Bertsekas · 2008
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New complexity results about nash equilibria
Vincent Conitzer and Tuomas Sandholm · 2008
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Peng Peng, Ying Wen, Yaodong Yang, Quan Yuan, Zhenkun Tang, Haitao Long, and Jun Wang · 2017
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Learning nash equilibrium for general-sum markov games from batch data
Julien Pérolat, Florian Strub, Bilal Piot, and Olivier Pietquin · 2017
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Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
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Online reinforcement learning in stochastic games
Chen-Yu Wei, Yi-Te Hong, and Chi-Jen Lu · 2017
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Wasserstein robust reinforcement learning
Mohammed Amin Abdullah, Hang Ren, Haitham Bou Ammar, Vladimir Milenkovic, Rui Luo, Mingtian Zhang, and Jun Wang · 2019
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Feature-based q-learning for two-player stochastic games
Zeyu Jia, Lin F Yang, and Mengdi Wang · 2019
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Independent policy gradient methods for competitive reinforcement learning
Constantinos Daskalakis, Dylan J Foster, and Noah Golowich · 2020
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A theoretical analysis of deep q-learning
Jianqing Fan, Zhaoran Wang, Yuchen Xie, and Zhuoran Yang · 2020
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A game–theoretic approach for generative adversarial networks
Barbara Franci and Sergio Grammatico · 2020
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Solving discounted stochastic two-player games with near-optimal time and sample complexity
Aaron Sidford, Mengdi Wang, Lin Yang, and Yinyu Ye · 2020
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An overview of multi-agent reinforcement learning from game theoretical perspective
Yaodong Yang and Jun Wang · 2020
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