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