Automatic ship collision avoidance using deep reinforcement learning with LSTM in continuous action spaces

Abstract

This paper presents an automatic collision avoidance algorithm for ships using a deep reinforcement learning (DRL) in continuous action spaces. Obstacle zone by target (OZT) is used to compute an area where a collision will happen in the future based on dynamic information of ships. Agents of DRL detects the approach of multiple ships using a virtual sensor called the grid sensor. Agents learned collision avoidance maneuvering through Imazu problem, which is a scenario set of ship encounter situations. In this study, we propose a new approach for collision avoidance with a longer safe passing distance using DRL. We develop a novel method named inside OZT that expands OZT to improve the consistency of learning. We redesign the network using the long short-term memory (LSTM) cell and carried out training in continuous action spaces to train a model with longer safe distance than the previous study. The bow cross range in collision detection proposed in this paper is effective to COLREGs-compliant collision avoidance. The trained model has passed all scenarios of Imazu problem. The model is also validated by a test scenario which includes more ships than each scenario of Imazu problem.