313 citations · 1.8k across the 83 of their papers we have counts for
7 papers · 1 filter
Policy Space Diversity for Non-Transitive Games
Jian Yao, Weiming Liu, Haobo Fu +4
Policy-Space Response Oracles (PSRO) is an influential algorithm framework for approximating a Nash Equilibrium (NE) in multi-agent non-transitive games. Many previous studies have…
Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game
Simin Li, Jun Guo, Jingqiao Xiu +6
In this study, we explore the robustness of cooperative multi-agent reinforcement learning (c-MARL) against Byzantine failures, where any agent can enact arbitrary, worst-case acti…
Regret-Minimizing Double Oracle for Extensive-Form Games
Xiaohang Tang, Le Cong Dinh, Stephen Marcus McAleer +1
By incorporating regret minimization, double oracle methods have demonstrated rapid convergence to Nash Equilibrium (NE) in normal-form games and extensive-form games, through algo…
On the Convergence of Fictitious Play: A Decomposition Approach
Yurong Chen, Xiaotie Deng, Chenchen Li +4
Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in -player games, which builds the foundation for modern…
Efficient Policy Space Response Oracles
Ming Zhou, Jingxiao Chen, Ying Wen +4
Policy Space Response Oracle methods (PSRO) provide a general solution to learn Nash equilibrium in two-player zero-sum games but suffer from two drawbacks: (1) the computation ine…
On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games
Xiaotie Deng, Ningyuan Li, David Mguni +2
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…
Is Nash Equilibrium Approximator Learnable?
Zhijian Duan, Wenhan Huang, Dinghuai Zhang +4
In this paper, we investigate the learnability of the function approximator that approximates Nash equilibrium (NE) for games generated from a distribution. First, we offer a gener…