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

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 [1]-[4]. 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.