论文标题
在连通环境中，强化学习启用学习的决策策略
Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment
论文作者
论文摘要
作为典型的车辆 - 物理系统（V-CPS），近年来，相连的自动化车辆吸引了越来越多的关注。本文着重讨论在连接环境中自动驾驶的决策策略（DM）策略。首先，制定了高速公路DM问题，其中，车辆可以通过无线网络交换信息。然后，在预定义的驾驶场景中，使用了两种经典的增强学习（RL）算法，即Q学习和DYNA。最后，分析了派生的DM策略在安全性和效率方面的控制性能。此外，RL算法的固有差异是在DM策略中体现和讨论的。
As a typical vehicle-cyber-physical-system (V-CPS), connected automated vehicles attracted more and more attention in recent years. This paper focuses on discussing the decision-making (DM) strategy for autonomous vehicles in a connected environment. First, the highway DM problem is formulated, wherein the vehicles can exchange information via wireless networking. Then, two classical reinforcement learning (RL) algorithms, Q-learning and Dyna, are leveraged to derive the DM strategies in a predefined driving scenario. Finally, the control performance of the derived DM policies in safety and efficiency is analyzed. Furthermore, the inherent differences of the RL algorithms are embodied and discussed in DM strategies.
