Multi-agent reinforcement learning-based dynamic task assignment for vehicles in urban transportation system

The task assignment for vehicles plays an important role in urban transportation system, which is the key to cost reduction and efficiency improvement. The development of information technology and the emergence of “sharing economy” create a more convenient transportation mode, but also bring a greater challenge to efficient operation of urban transportation system. On the one hand, considering the complex and dynamic environment of urban transportation, an efficient method for assigning transportation tasks to idle vehicles is desired. On the other hand, to meet the users' expectations on immediate response of vehicle, the task assignment problem with dynamic arrival remains to be resolved. In this study, we propose a dynamic task assignment method for vehicles in urban transportation system based on the multi-agent reinforcement learning (RL). The transportation task assignment problem is transformed into a stochastic game process from vehicles’ perspective, and then an extended actor-critic (AC) algorithm is employed to obtain the optimal strategy. Based on the proposed method, vehicles can independently make decisions in real time, thus eliminating a lot of communication cost. Compared with the methods based on first-come-first-service (FCFS) rule and classic contract net algorithm (CNA), the results show that the proposed method can obtain higher acceptance rate and profit rate in the service cycle.