most citedME-IGM: Individual-Global-Max in Maximum Entropy Multi-Agent Reinforcement Learning

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cs.LG2026

Continual Policy Consolidation for Lifelong Robot Learning

Qijun He, Yuxuan Li, Mingqi Yuan +4

Building a generalist robot policy requires continuously integrating new skills while preserving previously acquired behaviors. Directly optimizing a single policy over a growing t…

cs.LG2024★ 1 cited

ME-IGM: Individual-Global-Max in Maximum Entropy Multi-Agent Reinforcement Learning

Wen-Tse Chen, Yuxuan Li, Shiyu Huang +2

Multi-agent credit assignment is a fundamental challenge for cooperative multi-agent reinforcement learning (MARL), where a team of agents learn from shared reward signals. The Ind…