Multi-Agent Path Finding Using Imitation-Reinforcement Learning with Transformer

Abstract

Multi-Agent Path Finding is a problem of finding the optimal set of paths for multiple agents from the starting position to the goal without conflict, which is essential to large-scale robotic systems. Imitation and reinforcement learning are applied to solve the MAPF problem and have achieved certain results, which provides a feasible solution for the path planning problem of large-scale robot systems. The current method improves the performance of distributed strategy-guided agent planning paths in complex environments by introducing the communication between graph neural networks and agents but dramatically reduces the system's robustness. This paper develops a novel imitation reinforcement learning framework by introducing Transformer, which enables algorithms to perform well in complex environments without relying on communication between agents. Compared with its counterparts, experiments show that the policy trained by our method guides the agent to drive from the initial position to the goal without collision and achieve better performance.