[Submitted on 4 Nov 2026]
Title:Cooperative Multi-Agent Reinforcement Learning via Epigraph-Form Guided Exploration
View a PDF of the paper titled Cooperative Multi-Agent Reinforcement Learning via Epigraph-Form Guided Exploration, by Sungil Son and 3 other authors
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Abstract:Discovering cooperative behavior in multi-agent reinforcement learning (MARL) is challenging due to the combinatorial complexity of joint state-action spaces, which hinders the emergence of coordinated behaviors from trial-and-error alone. Intrinsic rewards are often used to aid discovery, but naively combining them with team objectives can distort the learning signal, compromising task performance. In this paper, we propose EFXPLORER, a constrained exploration framework that maximizes exploration objectives subject to a constraint that preserves established task performance. We solve the resulting problem using an epigraph reformulation that introduces adaptive exploration budgets. This approach separates intrinsic rewards from task objectives and regulates exploration through task feasibility. To further encourage diverse and temporally extended exploration, we incorporate a successor distance-based intrinsic reward that captures long-horizon dependencies. Empirically, our method outperforms strong baselines and induces novel cooperative strategies across SMAX, VMAS, and MPE benchmark suites.
Comments: NeurIPS 2026
Subjects:
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2611.07066 [cs.AI]
(or arXiv:2611.07066v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2611.07066
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hoseong Jung [view email] [v1] Wed, 4 Nov 2026 19:24:25 UTC (3,582 KB)
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