Regret Matching

Regret matching is a family of algorithms used to minimize cumulative regret in sequential decision-making problems, particularly within game theory and reinforcement learning. Current research focuses on improving the efficiency and stability of regret matching, including variations like Regret Matching+ and its application within frameworks like counterfactual regret minimization (CFR) and online mirror descent (OMD). These advancements aim to address issues such as distributional shifts in reward functions and the development of efficient algorithms for handling large-scale games and complex scenarios with multiple agents or groups. The impact of this research extends to improving the performance and robustness of AI agents in various applications, from game playing to online advertising and personalized recommendations.