Application of reinforcement learning to adaptive control of connected vehicles

Abstract

In this paper, we apply reinforcement learning to control of an auto-driven vehicle following after a platoon of human-driven vehicles. The auto-driven connected vehicle receives data from the preceding vehicles via a network. We consider the case where dynamics of the human-driven vehicles are unknown and that there are network delays between the human-driven vehicles and the auto-driven one. To stabilize the auto-driven vehicle, we introduce a linear discrete-time model with the delays of a platoon and propose a design method of an adaptive controller for the auto-driven vehicle using reinforcement learning. By simulation, it is shown that the proposed controller learns an optimal gain.