Crowd Perception Communication-Based Multi- Agent Path Finding With Imitation Learning

Deep reinforcement learning-based Multi-Agent Path Finding (MAPF) has gained significant attention due to its remarkable adaptability to environments. Existing methods primarily leverage multi-agent communication in a fully-decentralized framework to maintain scalability while enhancing information exchange among agents. However, as the number of agents and obstacles increases, the environment becomes more complex, making cooperation between agents becomes more difficult, and crowding occurs from time to time. To address these issues, we propose a decentralized planner C3PIL, which integrates a Controlled Communication mechanism for Crowd Perception and uses Imitation Learning to improve policy learning. C3PIL first introduces a crowd perception communication module that perceives environmental crowd information and incorporates it into the controlled communication. This effectively prevents and mitigates crowded situations. Furthermore, we employ generative adversarial imitation learning to learn a reward function from expert experiences. It reduces the possible misleading caused by the fixed reward function, improves the flexibility and diversity of agent behaviors, and ultimately enables agents to cooperate effectively. Finally, experimental results show that C3PIL not only outperforms previous learning-based MAPF methods, but also further enhances the cooperation of agents and significantly reduces crowding in complex environments.