Summary
the 2014 International Symposium on Nonlinear Theory and its Applications
2014
Session Number:A1L-B
Session:
Number:A1L-B3
Reinforcement Learning Based Search for Ships’ Courses Controlled by Safety
Masahiro Nakayama, Takeshi Kamio, Kunihiko Mitsubori, Takahiro Tanaka, Hisato Fujisaka,
pp.28-31
Publication Date:2014/9/14
Online ISSN:2188-5079
Summary:
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 causes 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.