AGiR: Mitigating Gift Over-Reliance in Mixed-Motive Games

Abstract:In this paper, we consider mixed-motive games (MMGs) which model various real-world multi-agent scenarios where self-interested agents must both cooperate and compete. We identify an issue with existing gifting methods, where agents become overly dependent on incoming gifts and fail to learn reward-yielding behaviors, a phenomenon we call gift over-reliance. To address this, we propose a refusal-augmented gifting framework that balances cooperation and competition in MMGs. Our approach introduces an adaptive refusal mechanism that allows agents to selectively reject a portion of received gifts from their policy-update signals, thereby mitigating gift over-reliance and preventing the agents from being exploited by other agents. This mechanism is gifting-method-agnostic, serving as a plug-in for existing schemes while enabling decentralized learning. We demonstrate that across diverse MMG environments and gifting schemes, our mechanism consistently reduces gift over-reliance and prevents lock-in to sacrificing policies, improving $\alpha$-fairness as a result, without necessitating major changes to existing training pipelines.

Multiagent Systems (cs.MA); Machine Learning (cs.LG)

arXiv:2611.02875 [cs.MA]

(or arXiv:2611.02875v1 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2611.02875 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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