Minimizing Weighted Counterfactual Regret with Optimistic Online Mirror Descent

arXiv:2404.13891v1 Announce Type: new
Abstract: Counterfactual regret minimization (CFR) is a family of algorithms for effectively solving imperfect-information games. It decomposes the total regret into counterfactual regrets, utilizing local regret minimization algorithms, such as Regret Matching (RM) or RM+, to minimize them. Recent research establishes a connection between Online Mirror Descent (OMD) and RM+, paving the way for an optimistic variant PRM+ and its extension PCFR+. However, PCFR+ assigns uniform weights for each iteration when determining regrets, leading to substantial …

arxiv counterfactual cs.ai cs.gt cs.lg type