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PARCO: Learning Parallel Autoregressive Policies for Efficient Multi-Agent Combinatorial Optimization

Autor: Berto, Federico, Hua, Chuanbo, Luttmann, Laurin, Son, Jiwoo, Park, Junyoung, Ahn, Kyuree, Kwon, Changhyun, Xie, Lin, Park, Jinkyoo

Multi-agent combinatorial optimization problems such as routing and scheduling have great practical relevance but present challenges due to their NP-hard combinatorial nature, hard constraints on the number of possible agents, and hard-to-optimize ob

Externí odkaz: http://arxiv.org/abs/2409.03811

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HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding

Autor: Tang, Huijie, Berto, Federico, Ma, Zihan, Hua, Chuanbo, Ahn, Kyuree, Park, Jinkyoo

Large-scale multi-agent pathfinding (MAPF) presents significant challenges in several areas. As systems grow in complexity with a multitude of autonomous agents operating simultaneously, efficient and collision-free coordination becomes paramount. Tr

Externí odkaz: http://arxiv.org/abs/2402.15546

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