We present PRIMAL3, an ultra-large-scale learning-based framework for multi-agent pathfinding (MAPF) that integrates reinforcement learning, topology-aware communication, LaCAM3-guided training, and PIBT-based action refinement. PRIMAL3 targets failures at topologically critical states, where agents must coordinate dec...
Cheng-Yang He, T. Duhan, Gadiel Sznaier Camps et al.· 1 citation
A Planner-Conditioned Diffusion Policy (PCDP) is proposed, trained on demonstrations from multiple planner styles with planner identity as an explicit conditioning input, enabling a single shared model to learn a multimodal trajectory distribution and generate diverse, controllable trajectory candidates from the same o...
This work introduces a unified RL formulation that jointly optimizes agent and environment policies, where the environment policy learns graph edge costs to provide global movement guidance via backward Dijkstra search and achieves significant improvements over the strong search-based planner, Causal-PIBT, across multi...
He Jiang, Jingtian Yan, Yulun Zhang et al.· 0 citations
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