Authors:
Jiyao Li
and
Vicki Allan
Affiliation:
Department of Computer Science, Utah State University, Logan, Utah, U.S.A
Keyword(s):
Autonomous Mobility on Demand (AMoD) Systems, Vehicle Repositioning, Task Assignment, Multiagent Reinforcement Learning (MARL).
Abstract:
Autonomous Mobility on Demand (AMoD) systems are a promising area in the emerging field of intelligent transportation systems. In this paper, we focus on the problem of how to dispatch a fleet of autonomous vehicles (AVs) within a city while balancing supply and demand. We first formulate the problem as a Markov Decision Process (MDP) of which the goal is to maximize the accumulated average reward, then propose the Multiagent Reinforcement Learning (MARL) framework. The Temporal-Spatial Dispatching Network (TSD-Net) that combines both policy and value network learns representation features facilitating spatial information with its temporal signals. The Batch Synchronous Actor Critic (BS-AC) samples experiences from the Rollout Buffer with replacement and trains parameters of the TSD-Net. Based on the state value from the TSD-Net, the Priority Destination Sampling Assignment (PDSA) algorithm defines orders’ priority by their destinations. Popular destinations are preferred as it is ea
sier for agents to find future work in a popular location. Finally, with the real-world city scale dataset from Chicago, we compare our approach to several competing baselines. The results show that our method is able to outperform other baseline methods with respect to effectiveness, scalability, and robustness.
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