Deep reinforcement learning algorithms for steering an underactuated ship

Abstract

Based on state-of-the-art deep reinforcement learning (Deep RL) algorithms, two controllers are proposed to pass a ship through a specified gate. Deep RL is a powerful approach to learn a complex controller which is expected to adapt to different situations of systems. This paper explains how to apply these algorithms to ship steering problem. The simulation results show advantages of these algorithms in reproducing reliable and stable controllers.