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 is a challenging computational problem that typically requires to find collision-free paths for multiple agents in a shared environment. Solving MAPF optimally is NP-hard%2C yet efficient solutions are critical for numerous applications%2C including automated warehouses and transportation systems. Recently%2C learning-based approaches to MAPF have gained attention%2C particularly those leveraging deep reinforcement learning. Following current trends in machine learning%2C we have created a foundation model for the MAPF problems called MAPF-GPT. Using imitation learning%2C we have trained a policy on a set of pre-collected sub-optimal expert trajectories that can generate actions in conditions of partial observability without additional heuristics%2C reward functions%2C or communication with other agents. The resulting MAPF-GPT model demonstrates zero-shot learning abilities when solving the MAPF problem instances that were not present in the training dataset. We show that MAPF-GPT notably outperforms the current best-performing learnable-MAPF solvers on a diverse range of problem instances and is efficient in terms of computation (in the inference mode).?quality=80&w=800)
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Related Visuals
- (PDF) MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale
- MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale | AI ...
- MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale | AI ...
- [논문 리뷰] MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale
- GitHub - Elucidation/mapf-multiagent-robot-planning: Multi-Agent ...
- GitHub - infinitusposs/Multi-Agent-Path-Finding-MAPF-with-Heuristics
- MAPF-GPT: A Decentralized and Scalable AI Approach to Multi-Agent ...
- MAPF-GPT: A Decentralized and Scalable AI Approach to Multi-Agent ...
- aandreychuk/MAPF-GPT · Datasets at Hugging Face
- Multi-Agent Pathfinding (MAPF) with Continuous Time | DeepAI