  Previous studies on automatic berthing systems based on artificial neural
network (ANN) showed great berthing performance by training the ANN with ship
berthing data as training data. However, because the ANN requires a large
amount of training data to yield robust performance, the ANN-based automatic
berthing system is somewhat limited due to the difficulty in obtaining the
berthing data. In this study, to overcome this difficulty, the automatic
berthing system based on one of the reinforcement learning (RL) algorithms,
proximal policy optimization (PPO), is proposed because the RL algorithms can
learn an optimal control policy through trial-and-error by interacting with a
given environment and does not require any pre-obtained training data, where
the control policy in the proposed PPO-based automatic berthing system controls
revolutions per second (RPS) and rudder angle of a ship. Finally, it is shown
that the proposed PPO-based automatic berthing system eliminates the need for
obtaining the training dataset and shows great potential for the actual
berthing application.
