论文标题
智能城市新兴出行系统的滞后Q学习协调框架
A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities
论文作者
论文摘要
连接和自动化的车辆（CAV）可以减轻交通拥堵，空气污染并改善安全性。在本文中，我们在无信号的交叉路口为CAV提供了一个分散的协调框架，以最大程度地减少行进时间并提高燃油效率。我们采用了一种简单而强大的增强学习方法，这是一种称为Q学习的偏离时间差异学习，并通过协调机制增强了解决此问题。然后，我们集成了第一届排队排队政策，以提高系统的性能。我们通过基于Pontryagin的最低原理进行仿真和比较来证明我们提出的方法的功效。
Connected and automated vehicles (CAVs) can alleviate traffic congestion, air pollution, and improve safety. In this paper, we provide a decentralized coordination framework for CAVs at a signal-free intersection to minimize travel time and improve fuel efficiency. We employ a simple yet powerful reinforcement learning approach, an off-policy temporal difference learning called Q-learning, enhanced with a coordination mechanism to address this problem. Then, we integrate a first-in-first-out queuing policy to improve the performance of our system. We demonstrate the efficacy of our proposed approach through simulation and comparison with the classical optimal control method based on Pontryagin's minimum principle.
