Model-based Reinforcement Learning for Ship Path Following with Disturbances

Abstract

Modelling and control of ships is a challenging task due to their intrinsic nonlinearities and high uncertainty. In the complex and dynamic sea environment, the efficacy of well-designed motion controllers diminishes significantly. To achieve optimal performance, high-performance motion control systems must possess the ability to adapt to diverse working conditions, repel external disturbances, and incorporate learning capabilities. In addressing critical computational challenges encountered in the real-world deployment of autonomous driving agents, Reinforcement Learning (RL) methods have been effectively employed. This paper proposed a reinforcement learning control method for ship path following based on an environmental disturbance model. RL is utilized to learn the dynamic properties of the system to resist environmental disturbances. Experimental results show that the method can follow the path with acceptable accuracy and high robustness.