A dual-graph, reinforcement-and-imitation learning framework for MAPF that scales to 100,000 agents, with results close to search-based solvers on random maps.
Feasibility study: Moving non-homogeneous teams in congested video game environ- ments,
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PRIMAL3: Pathfinding via Reinforcement and Imitation Multi-Agent Learning - Leveraging LaCAM3
A dual-graph, reinforcement-and-imitation learning framework for MAPF that scales to 100,000 agents, with results close to search-based solvers on random maps.