PRIMAL3: Multi-Agent Pathfinding via Reinforcement and Imitation Learning with LaCAM3
Original title:PRIMAL3: Pathfinding via Reinforcement and Imitation Multi-Agent Learning - Leveraging LaCAM3
PRIMAL3 is a learning-based framework for ultra-large-scale multi-agent pathfinding. It combines reinforcement learning, imitation learning, topology-aware communication, LaCAM3-guided training, and PIBT-based action refinement. Agents use features related to cut vertices, dead ends, shortest paths, and blocking. Separate graphs model compatible following and conflicting movement directions. The paper reports scalability to city-level instances with up to 100,000 agents, along with ablations and physical-robot experiments. These claims come from the supplied abstract; detailed datasets, baselines, metrics, and experimental protocols require verification in the full paper.
Why it's worth reading
MAPF is moving toward city-scale coordination, and PRIMAL3 offers a concrete hybrid design that combines learned policies with structured planning. Its 100,000-agent and physical-robot claims make the full evaluation details especially important now.