Attention-Cooperated Reinforcement Learning for Multi-agent Path Planning

Multi-agent path finding (MAPF), in multi-agent systems, is a challenging and meaningful problem, in which all agents are required to effectively reach their goals concurrently with not colliding with each other and avoiding the obstacles. Effective extraction from the agent's observation, effective utilization of historical information, and efficient communication with neighbor agents are the challenges to completing the cooperative task. To tackle these issues, in this paper, we propose a well-designed model, which utilizes the local states of nearby agents and obstacles and outputs an optimal action for each agent to execute. Our approach has three major components: 1) observation encoder which uses CNN to extract local partial observation and GRU to make full use of historical information, 2) communication block which uses attention mechanism to combine the agent's partial observation with its neighbors, and 3) decision block with the purpose to output the final action policy. Based on the three major components, all agents formulate their own decentralized policies to apply. Finally, we use success rate and extra time rate to measure our approach and other well-known algorithms. The results show that our method outperforms the baselines, demonstrating the efficiency and effectiveness of our approach, especially in the case of large scale in the world.