A LOAD BALANCE PERSONALIZED PATH FINDING WITH MULTIPLE-AGENT DEEP REINFORCEMENT LEARNING

Abstract

Personalized path-finding allows a user to search for a travel path that can visit its several regions of interest (ROIs). Existing independent searching methods may bring many users to ROIs simultaneously, such as popular attractions in scenic, and thus induce traffic jams. Therefore, it is necessary to find a personalized travel path considering the load balance of traffic flow. However, it is challenging to ensure load balancing on the road and ROIs while allowing each user to visit their ROIs. In this paper, we propose a personalized path-finding method with a multi-agent path-finding (MAPF) framework. Based on the MAPF, it allows users to find the path independently, and we designed a reward to guide the agent simultaneously to navigate to the destination and ROIs for personalized travel. We also improve the architecture of MAPF to guide the agents in learning the load balance through a centralized value network. We evaluated the algorithm with up to 1024 agents on randomly generated road network graphs and compared it against state-of-the-art MAPF planners. We also trained the agent using imitation learning and validated our framework on real-world datasets. Experimental results show that our algorithm is efficient and better than the existing algorithms.