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Reinforcement learning for multi-agent games has attracted lots of attention recently.
Linear programming and sequential decisions
Alan S Manne · 1960
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Nash q-learning for general-sum stochastic games
Junling Hu and Michael P Wellman · 2003
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Convex optimization
Stephen P Boyd and Lieven Vandenberghe · 2004
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Large population stochastic dynamic games: closed-loop mckean-vlasov systems and the nash certainty equivalence principle
Minyi Huang, Roland P Malhamé, and Peter E Caines · 2006
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Mean field games
Jean-Michel Lasry and Pierre-Louis Lions · 2007
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Near-optimal regret bounds for reinforcement learning
Peter Auer, Thomas Jaksch, and Ronald Ortner · 2008
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Minimax pac bounds on the sample complexity of reinforcement learning with a generative model
Mohammad Gheshlaghi Azar, Rémi Munos, and Hilbert J Kappen · 2013
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Revisiting frank-wolfe: Projection-free sparse convex optimization
Martin Jaggi · 2013
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Primer on monotone operator methods
Ernest K Ryu and Stephen Boyd · 2016
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Minimax regret bounds for reinforcement learning
Mohammad Gheshlaghi Azar, Ian Osband, and Rémi Munos · 2017
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A unified game-theoretic approach to multiagent reinforcement learning
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Pérolat, David Silver, and Thore Graepel · 2017
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Is q-learning provably efficient?
Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, and Michael I Jordan · 2018
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Markov–Nash equilibria in mean-field games with discounted cost
Naci Saldi, Tamer Basar, and Maxim Raginsky · 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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Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
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Fitted q-learning in mean-field games
Berkay Anahtarci, Can Deha Kariksiz, and Naci Saldi · 2019
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On the implementation of a primal-dual algorithm for second order time-dependent mean field games with local couplings
Luis Briceno-Arias, Dante Kalise, Ziad Kobeissi, Mathieu Lauriere, A Mateos González, and Francisco J Silva · 2019
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From the master equation to mean field game limit theory: a central limit theorem
François Delarue, Daniel Lacker, and Kavita Ramanan · 2019
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Actor-critic provably finds nash equilibria of linear-quadratic mean-field games
Zuyue Fu, Zhuoran Yang, Yongxin Chen, and Zhaoran Wang · 2019
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Learning mean-field games
Xin Guo, Anran Hu, Renyuan Xu, and Junzi Zhang · 2019
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A generalized training approach for multiagent learning
Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Perolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, et al · 2019
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Algaedice: Policy gradient from arbitrary experience
Ofir Nachum, Bo Dai, Ilya Kostrikov, Yinlam Chow, Lihong Li, and Dale Schuurmans · 2019
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Reinforcement learning in stationary mean-field games
Jayakumar Subramanian and Aditya Mahajan · 2019
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Near-optimal reinforcement learning with self-play
Yu Bai, Chi Jin, and Tiancheng Yu · 2020
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Mean-field games of optimal stopping: a relaxed solution approach
Géraldine Bouveret, Roxana Dumitrescu, and Peter Tankov · 2020
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On the convergence of model free learning in mean field games
Romuald Elie, Julien Perolat, Mathieu Laurière, Matthieu Geist, and Olivier Pietquin · 2020
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Finite-time last-iterate convergence for multi-agent learning in games
Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, and Michael Jordan · 2020
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Splitting methods for a class of non-potential mean field games
Siting Liu and Levon Nurbekyan · 2020
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Fictitious play for mean field games: Continuous time analysis and applications
Sarah Perrin, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Romuald Elie, and Olivier Pietquin · 2020
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Osqp: An operator splitting solver for quadratic programs
Bartolomeo Stellato, Goran Banjac, Paul Goulart, Alberto Bemporad, and Stephen Boyd · 2020
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Independent policy gradient for large-scale markov potential games: Sharper rates, function approximation, and game-agnostic convergence
Dongsheng Ding, Chen-Yu Wei, Kaiqing Zhang, and Mihailo Jovanovic · 2022
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Optimization frameworks and sensitivity analysis of stackelberg mean-field games
Xin Guo, Anran Hu, and Jiacheng Zhang · 2022
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MF-OMO: An optimization formulation of mean-field games
Xin Guo, Anran Hu, and Junzi Zhang · 2022
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Welfare maximization in competitive equilibrium: Reinforcement learning for markov exchange economy
Zhihan Liu, Miao Lu, Zhaoran Wang, Michael Jordan, and Zhuoran Yang · 2022
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Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E Taylor, and Nidhi Hegde · 2020
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Partially observable mean field reinforcement learning
Sriram Ganapathi Subramanian, Matthew E Taylor, Mark Crowley, and Pascal Poupart · 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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Mopo: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
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Multi-vehicle routing problems with soft time windows: A multi-agent reinforcement learning approach
Ke Zhang, Fang He, Zhengchao Zhang, Xi Lin, and Meng Li · 2020
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On the theory of policy gradient methods: Optimality, approximation, and distribution shift
Alekh Agarwal, Sham M Kakade, Jason D Lee, and Gaurav Mahajan · 2021
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Reinforcement learning for mean field games, with applications to economics
Andrea Angiuli, Jean-Pierre Fouque, and Mathieu Lauriere · 2021
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Paul Muller, Romuald Elie, Mark Rowland, Mathieu Lauriere, Julien Perolat, Sarah Perrin, Matthieu Geist, Georgios Piliouras, Olivier Pietquin, and Karl Tuyls · 2022
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Decentralized mean field games
Sriram Ganapathi Subramanian, Matthew E Taylor, Mark Crowley, and Pascal Poupart · 2022
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Policy mirror ascent for efficient and independent learning in mean field games
Batuhan Yardim, Semih Cayci, Matthieu Geist, and Niao He · 2022
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Independent learning in mean-field games: Satisficing paths and convergence to subjective equilibria
Bora Yongacoglu, Gürdal Arslan, and Serdar Yüksel · 2022
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Andrea Angiuli, Jean-Pierre Fouque, Mathieu Laurière, and Mengrui Zhang · 2023
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Linear programming fictitious play algorithm for mean field games with optimal stopping and absorption
Roxana Dumitrescu, Marcos Leutscher, and Peter Tankov · 2023
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Learning sparse graphon mean field games
Christian Fabian, Kai Cui, and Heinz Koeppl · 2023
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Mfglib: A library for mean-field games
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A general framework for learning mean-field games
Xin Guo, Anran Hu, Renyuan Xu, and Junzi Zhang · 2023
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Markov α \alpha -potential games: Equilibrium approximation and regret analysis
Xin Guo, Xinyu Li, Chinmay Maheshwari, Shankar Sastry, and Manxi Wu · 2023
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Variance reduced value iteration and faster algorithms for solving markov decision processes
Aaron Sidford, Mengdi Wang, Xian Wu, and Yinyu Ye · 2023
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Stateless mean-field games: A framework for independent learning with large populations
Batuhan Yardim, Semih Cayci, and Niao He · 2023
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Oracle-free reinforcement learning in mean-field games along a single sample path
Muhammad Aneeq Uz Zaman, Alec Koppel, Sujay Bhatt, and Tamer Basar · 2023
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Learning regularized monotone graphon mean-field games
Fengzhuo Zhang, Vincent YF Tan, Zhaoran Wang, and Zhuoran Yang · 2023
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Optimistic policy gradient in multi-player markov games with a single controller: Convergence beyond the minty property
Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina, and Tuomas Sandholm · 2024
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Mf-omo: An optimization formulation of mean-field games
Xin Guo, Anran Hu, and Junzi Zhang · 2024
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Model-based rl for mean-field games is not statistically harder than single-agent rl
Jiawei Huang, Niao He, and Andreas Krause · 2024
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On the statistical efficiency of mean-field reinforcement learning with general function approximation
Jiawei Huang, Batuhan Yardim, and Niao He · 2024
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Zero-sum polymatrix markov games: Equilibrium collapse and efficient computation of nash equilibria
Fivos Kalogiannis and Ioannis Panageas · 2024
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Monotone inclusion methods for a class of second-order non-potential mean-field games
Levon Nurbekyan, Siting Liu, and Yat Tin Chow · 2024
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When is mean-field reinforcement learning tractable and relevant?
Batuhan Yardim, Artur Goldman, and Niao He · 2024
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