关键词: 驻站, 多智能体增强学习, 多智能体系统, 控制策略

Abstract: Vehicle holding is a commonly used strategy among a variety of control strategies in transit operation for improving transit service reliability, whose implementation needs dynamic decision-making in an interactive and stochastic system environment. This paper introduces a novel use of a reinforcement learning framework to obtain vehicle holding autonomous control strategy in cooperative multi-agent system. Transit operation control model is developed based on multi-agent system. In the multi-agent reinforcement learning framework, each bus is modeled as an  independent agent with learning abilities, for which the state, actions and reward are defined and a coordination mechanism for multiple bus agents is designed to obtain a joint holding actions. The hysteretic Q-learning algorithm is used to solve this holding problem. From the simulation experiments, the results illustrate that the proposed approach is able to prevent buses from bunching and regulate bus headway.

Key words: bus holding, multi-agent reinforcement learning, multi-agent system, control strategy