Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning

Paper recorded by Signals 4 on 2026-10-13 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-13 on arXiv · recorded by Signals 4 on 2026-10-14

Category: cs.AI · 人工智能 · first seen 2026-10-14

Abstract

In cooperative Multi-Agent Reinforcement Learning (MARL), agents must learn to coordinate their actions to achieve shared objectives. A key indicator of effective coordination is behavioral diversity, i.e., the agents' potential to enact distinct yet complementary control policies given the same set of observations. Although behavioral diversity can enable role specialization and richer joint beha

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