Affiliations: Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania
Correspondence:
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Corresponding author: Gintautas Dzemyda, Institute of Data Science and Digital Technologies, Vilnius University, Akademijos Str. 4, LT-08412 Vilnius, Lithuania. E-mail: [email protected].
Abstract: Safe navigation at sea is more important than ever. Cargo is usually transported by vessel because it makes economic sense. However, marine accidents can cause huge losses of people, cargo, and the vessel itself, as well as irreversible ecological disasters. These are the reasons to strive for safe vessel navigation. The navigator shall ensure safe vessel navigation. He must plan every maneuver and act safely. At the same time, he must evaluate and predict the actions of other vessels in dense maritime traffic. This is a complicated process and requires constant human concentration. It is a very tiring and long-lasting duty. Therefore, human error is the main reason of collisions between vessels. In this paper, different reinforcement learning strategies have been explored in order to find the most appropriate one for the real-life problem of ensuring safe maneuvring in maritime traffic. An experiment using different algorithms was conducted to discover a suitable method for autonomous vessel navigation. The experiments indicate that the most effective algorithm (Deep SARSA) allows reaching 92.08% accuracy. The efficiency of the proposed model is demonstrated through a real-life collision between two vessels and how it could have been avoided.
Keywords: Marine traffic, reinforcement learning, Q-learning, SARSA, Monte Carlo, Deep SARSA
