Repeated Policy Regret metric captures adaptive opponent behavior in game theory
June 3, 2026
Repeated Policy Regret (RP-Regret) is a new game-theoretic metric for regret minimization where opponents adapt based on play history, a scenario standard external regret fails to model. The framework enables stronger comparators and fewer constraints on opponents while still allowing equilibrium discovery.
HOW THIS AFFECTS YOU
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researcherRP-Regret provides a more realistic evaluation framework for multi-agent RL settings where agents respond to history, relevant to training competitive or cooperative LLM agents.