  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.
