基于优先级重规划的通信多智能体路径规划

Multi-agent reinforcement learning (MARL), with its outstanding generalization capabilities and rapid computation speed, has become an effective tool for solving tasks with high real-time requirements and has been extensively researched and applied in multi-agent path finding (MAPF) problems. Nevertheless, MARL-based MAPF solvers still face the challenge of planning collision-free paths for agents when the agent density is extremely high. Most existing learning-based MAPF solvers tend to pause the movement of an agent as an alternative action when a potential collision is predicted, lacking effective coordination mechanisms among agents, which may lead to deadlocks. To address this challenge, this study proposes a method that combines reinforcement learning with a communication network equipped with an attention mechanism, aimed at optimizing the solution of the MAPF problem. Moreover, to reduce the occurrence of deadlocks in high-density agent environments, this study has designed a set of action detection and replanning strategies. An internal reward mechanism has also been introduced to encourage agents to explore the environment and accelerate the process of reaching their target locations. Experimental validation shows that the proposed method significantly outperforms existing advanced learning-based MAPF methods in terms of accuracy and performs comparably to near-optimal solvers in multiple environments.