4 papers
Last-Iterate Convergence in Adaptive Regret Minimization for Approximate Extensive-Form Perfect Equilibrium
Hang Ren, Xiaozhen Sun, Tianzi Ma +2
The Nash Equilibrium (NE) assumes rational play in imperfect-information Extensive-Form Games (EFGs) but fails to ensure optimal strategies for off-equilibrium branches of the game…
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning
Shuhan Qi, Shuhao Zhang, Xiaohan Hou +3
Multi-agent reinforcement learning for incomplete information environments has attracted extensive attention from researchers. However, due to the slow sample collection and poor s…
RLCFR: Minimize Counterfactual Regret by Deep Reinforcement Learning
Huale Li, Xuan Wang, Fengwei Jia +4
Counterfactual regret minimization (CFR) is a popular method to deal with decision-making problems of two-player zero-sum games with imperfect information. Unlike existing studies…
Solving imperfect-information games via exponential counterfactual regret minimization
Huale Li, Xuan Wang, Shuhan Qi +4
In general, two-agent decision-making problems can be modeled as a two-player game, and a typical solution is to find a Nash equilibrium in such game. Counterfactual regret minimiz…