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Potential games are arguably one of the most important and widely studied classes of normal form games.
Stochastic games
L. S. Shapley · 1953
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Potential Games
D. Monderer and L. S. Shapley · 1996
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Worst-case equilibria
E. Koutsoupias and C. Papadimitriou · 1999
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Dynamic Programming and Optimal Control
Dimitri P. Bertsekas · 2000
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Approximately Optimal Approximate Reinforcement Learning
S. Kakade and J. Langford · 2002
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How bad is selfish routing?
Tim Roughgarden and Éva Tardos · 2002
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Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games
X. Wang and T. Sandholm · 2002
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To queue or not to queue: Equilibrium behavior in queueing systems
R. Hassin and M. Haviv · 2003
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A comprehensive survey of multiagent reinforcement learning
Lucian Busoniu, Robert Babuska, and Bart De Schutter · 2008
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Multiplicative updates outperform generic no-regret learning in congestion games
Robert Kleinberg, Georgios Piliouras, and Éva Tardos · 2009
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Robust stochastic approximation approach to stochastic programming
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro · 2009
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State based potential games
J. R. Marden · 2012
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Convex optimization: Algorithms and complexity
Sébastien Bubeck · 2015
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Natural selection as an inhibitor of genetic diversity: Multiplicative weights updates algorithm and a conjecture of haploid genetics
Ruta Mehta, Ioannis Panageas, and Georgios Piliouras · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Cited alongside, same era.
Intrinsic robustness of the price of anarchy
T. Roughgarden · 2015
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Fast convergence of regularized learning in games
Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, and Robert E. Schapire · 2015
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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, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
Superhuman ai for multiplayer poker
Noam Brown and Tuomas Sandholm · 2019
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First-order methods almost always avoid strict saddle points
Jason D Lee, Ioannis Panageas, Georgios Piliouras, Max Simchowitz, Michael I Jordan, and Benjamin Recht · 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, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander S. Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom L. 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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Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
K. Zhang, Z. Yang, and T. Başar · 2019
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Learning with bandit feedback in potential games
Johanne Cohen, Amélie Héliou, and Panayotis Mertikopoulos · 2017
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Damek Davis and Dmitriy Drusvyatskiy · 2018
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Cycles in adversarial regularized learning
Panayotis Mertikopoulos, Christos Papadimitriou, and Georgios Piliouras · 2018
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Openai five
OpenAI · 2018
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Multiplicative weights update as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always
Ioannis Panageas, Georgios Piliouras, and Xiao Wang · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis · 2018
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Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 2018
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A. Agarwal, S. M. Kakade, J. D. Lee, and G. Mahajan · 2020
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Cooperative multi-player bandit optimization
I. Bistritz and N. Bambos · 2020
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Open Problems in Cooperative AI
A. Dafoe, E. Hughes, Y. Bachrach, T. Collins, K. R. McKee, J. Z. Leibo, K. Larson, and T. Graepel · 2020
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Independent Policy Gradient Methods for Competitive Reinforcement Learning
C. Daskalakis, D.J. Foster, and N. Golowich · 2020
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No-regret learning and mixed nash equilibria: They do not mix
Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Panayotis Mertikopoulos, and Georgios Piliouras · 2020
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Learning in matrix games can be arbitrarily complex
Gabriel P Andrade, Rafael Frongillo, and Georgios Piliouras · 2021
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Cooperative ai: machines must learn to find common ground
A. Dafoe, Y. Bachrach, G. Hadfield, E. Horvitz, K. Larson, and T. Graepel · 2021
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Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, and Haipeng Luo · 2021
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Provably efficient policy gradient methods for two-player zero-sum markov games
Yulai Zhao, Yuandong Tian, Jason D. Lee, and Simon S. Du · 2021
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