Abstract: Multi-agent path finding(MAPF) algorithms based on deep reinforcement learning(DRL) have received extensive attention due to their good adaptability to the environment.With the growth of application demands in related fields,map structures have become increasingly intricate,and the number of agents has continued to increase,leading to a significant rise in the difficulty of interactions among agents.Existing methods mainly enhance information exchange among agents through communication mechanisms,but in high-density scenarios,it is still difficult to avoid agents getting stuck in regional congestion or generating reciprocating oscillations,which in turn affects the efficiency of overall path planning.To address the above issues,a multi-agent pathfinding method named CAOR(Congestion-Avoidance and Oscillation-Resolution Enabled Multi-Agent Pathfinding) is proposed.This method comprehensively considers the density of objects around the agents and the activity of adjacent agents,and accordingly redesigns the reward function to guide the agents to avoid congested areas.Meanwhile,for the oscillation problem,CAOR identifies oscillation states by detecting the reciprocating movements of each agent,attaches oscillation labels to the agents that exhibit oscillations,and applies a search algorithm to these labeled agents to replan their routes.Experimental results show that in structured maps,when the number of agents does not exceed 64,the success rate of CAOR reaches 100%.In large-scale scenarios,compared with existing learning-based methods,CAOR also shows better solution performance.

Key words: Deep reinforcement learning, Path planning, Congestion-avoidance, Selective communication, Oscillation-resolution