Toward Adaptive and Coordinated Transportation Systems: A Multi-Personality Multi-Agent Meta-Reinforcement Learning Framework

Advancements in Intelligent Transportation Systems (ITS) have led to innovative solutions for planning optimization, efficiency enhancement, and resource allocation in transportation networks, which are demonstrated in applications such as smart parking lot management and electric vehicle (EV) charging station allocation, where improved decision-making and system-wide optimization have been achieved. However, as these systems evolve, the demand for better adaptability and coordination continues to grow to maximize their overall effectiveness and efficiency. To achieve this, we propose the Multi-Personality Multi-Agent Meta-Reinforcement Learning (MPMA-MRL) framework. This approach incorporates multiple meta-trained, meta-tested explainable personality policies, which are deployed to each agent. A personality selector is trained and deployed on each agent to optimize the overall performance. MPMA-MRL is superior than traditional methods in terms of the adaptability and coordination in ITS by leveraging improved information from the environment, more practical coordination among agents, faster adaptation speed to intermediate tasks, and more appropriate allocation and planning. The proposed framework is evaluated in the applications of parking lot optimization and EV charging station allocation. Its broader impact on multi-agent smart systems is analyzed to demonstrate its generalizability. The results demonstrate that in parking lot optimization, MPMA-MRL significantly reduces the time required to direct all vehicles to available parking spots. In EV charging station allocation, MPMA-MRL effectively minimizes waiting times at charging stations. Moreover, in both applications, MPMA-MRL exhibits enhanced adaptability to previously unseen scenarios, improving its applicability.