AI RESEARCH
Episodic Memory Temporal Consistency for Cooperative Multi-Agent Reinforcement Learning
arXiv CS.LG
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ArXi:2606.04492v1 Announce Type: new Cooperative Multi-Agent Reinforcement Learning (MARL) frequently suffers from severe reward sparsity and exploration bottlenecks. While episodic memory mechanisms mitigate these issues by reusing high-return trajectories, they often trap agents in local optima due to unconstrained incentive distribution and semantic representation collapse. To address this, we propose Episodic Memory Temporal Consistency (EMTC), a framework that robustly constructs and selectively leverages historical experiences.