March 13, 2024, 4:43 a.m. | Huijie Tang, Federico Berto, Jinkyoo Park

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.07559v1 Announce Type: cross
Abstract: Multi-Agent Reinforcement Learning (MARL) based Multi-Agent Path Finding (MAPF) has recently gained attention due to its efficiency and scalability. Several MARL-MAPF methods choose to use communication to enrich the information one agent can perceive. However, existing works still struggle in structured environments with high obstacle density and a high number of agents. To further improve the performance of the communication-based MARL-MAPF solvers, we propose a new method, Ensembling Prioritized Hybrid Policies (EPH). We first propose …

abstract agent arxiv attention communication cs.ai cs.lg cs.ma cs.ro efficiency environments however hybrid information multi-agent path pathfinding reinforcement reinforcement learning scalability struggle the information type

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