Reinforcement learning-based autonomous generation technology of loading scheme for product oil tanker

  Objectives  This paper aims to study the automatic generation technology of loading and unloading schemes for the liquid cargo tank of oil tanker based on reinforcement learning.

  Methods  Using the cargo capacity of an actual operating oil tanker as input and the loading rates of the cargo tank and ballast water tank as the targets, an intelligent agent and environment were built based on Unity ML-Agents. The agent was trained using the PyTorch framework, and a reward function calculation method that comprehensively considers the loading time and the change in the trim amplitude was proposed. Finally, the example analysis were carried out to validate the validity of the proposed method.

  Results  The results show that, the trained agent can learn good strategies and achieve autonomous generation of liquid cargo tank loading schemes.

  Conclusions  The study showed that applying reinforcement learning to solve the problem of autonomous generation of liquid cargo tank loading schemes under multi-objective conditions is reasonable and feasible.