Multi-agent reinforcement learning system to find efficient courses for ships

Abstract

Although the ship transportation is important for low cost mass transit, the optimality of ships' courses and the interaction between maneuvering actions have not been sufficiently discussed yet. In order to brisk up these discussions, we have developed multi-agent reinforcement learning system (MARLS) to find ships' courses 111-141. Although our basic MARLS [3] can keep navigation rules [5], it may get inefficient courses including larger avoidance of collisions between ships. In this paper, we clarify the cause of this problem and propose a new MARLS controlled by the safety to overcome it. From numerical experiments, we have confirmed that our proposed MARLS can get more efficient courses than our basic MARLS.