Control of an Unmanned Surface Vehicle Based on Adaptive Dynamic Programming and Deep Reinforcement Learning

This paper presents a low-level controller for an unmanned surface vehicle based on Adaptive Dynamic Programming (ADP) and deep reinforcement learning (DRL). The model-based algorithm Back-propagation Through Time and a simulation of the mathematical model of the vessel are implemented to train a deep neural network to drive the surge speed and yaw dynamics. The controller presents successful simulation results validating the feasibility of the proposed strategy and contributes to the diversity of validated applications of ADP and DRL control strategies.