arXiv:2606.06486v1 Announce Type: new Abstract: In this paper, we study regret minimization in repeated games with \emph{adaptive} opponents who can respond based on histories of play. The standard metric of \emph{external regret} in online learning is known to fail to capture such adaptivity. To account for players' counterfactual reasoning, we introduce {\tt Repeated Policy Regret (RP-Regret)}, a game-theoretic metric that measures the difference between the \emph{realized} and the \emph{best-in-hindsight} accumulated utility when all players can \emph{respond} to the history of play. Compar
The proliferation of complex AI systems necessitates more robust theoretical frameworks for understanding and managing their interactions, particularly in competitive or adversarial settings.
This research provides a foundational theoretical metric for designing and evaluating multi-agent AI systems, addressing limitations of existing online learning metrics that do not account for adaptive opponents.
The introduction of 'Repeated Policy Regret (RP-Regret)' offers a more sophisticated way to measure performance in repeated games with adaptive agents, potentially leading to more advanced and resilient AI agent designs.
- · AI agents researchers
- · Game theory practitioners
- · AI ethics and safety organizations
- · Developers relying solely on external regret
- · Simplistic multi-agent AI frameworks
Improved theoretical understanding of adaptive multi-agent AI systems.
Development of more robust and strategic AI agents that can anticipate and react to sophisticated opponents.
Enhanced capabilities for AI systems in complex, real-world strategic environments such as finance, defense, or logistics, where interactions are dynamic and adaptive.
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