Network-aware multi-agent reinforcement learning for the vehicle navigation problem

Abstract

Traffic congestion is characterized by longer trip times, and increased air pollution. In a static road network, the travel time to a destination is constant and can be computed using the shortest path first algorithm (SPF). However, road network conditions are dynamic, rendering the SPF to perform sub-optimally at times. In addition, in a realistic multiple-vehicle scenario, the SPF routing algorithm can cause congestion by routing all vehicles through the same shortest path. In this paper, we propose a network-aware multi-agent reinforcement learning model for addressing this problem. Our key idea is to assign an RL agent to intersections. Each RL agent operates as a router agent and is responsible for providing routing instructions to approaching vehicles. When a vehicle reaches an intersection, it submits a routing query to the RL agent consisting of its final destination. The RL agent generates a routing response based on (i) the destination, (ii) the current state of the road network, and (iii) routing policies learned by cooperating with other neighboring RL agents. Our experimental evaluation shows that the proposed MARL model outperforms the SPF algorithm by (up to) 20.2% in average travel time.