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Regret Minimization with Adaptive Opponents in Repeated Games

paperactiveregret-minimization-with-adaptive-opponents-in-repeated-games-514f5b61·1 events·first seen Jun 5, 2026

Aliases: Regret Minimization with Adaptive Opponents in Repeated Games

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4arXiv · cs.LG·Jun 5, 2026·source ↗

Repeated Policy Regret (RP-Regret): Regret minimization against adaptive opponents in repeated games

This arXiv paper introduces Repeated Policy Regret (RP-Regret), a new game-theoretic metric for regret minimization in repeated games where opponents can adapt based on play history — a setting where standard external regret fails. The authors prove necessary conditions for sublinear RP-Regret and propose three algorithms to minimize it, including oracle-based, linearized surrogate, and slow-opponent variants. When all players minimize RP-Regret, certain subgame perfect equilibria can be learned, and experiments show more cooperative outcomes in games like Stag-Hunt.

Evaluation and Benchmarking Repeated Policy Regret (RP-Regret)Regret Minimization with Adaptive Opponents in Repeated Games