Abstract Path following in marine vessels presents significant challenges due to complex wave dynamics. This paper addresses both path-following accuracy and fuel consumption optimization through reduced rudder fluctuations. A novel multi-objective Pareto optimization approach is introduced to determine optimal reward function weights for deep reinforcement learning, replacing conventional trial-and-error methods. The study presents a hybrid learning framework that integrates Generative Adversarial Imitation Learning (GAIL) with Proximal Policy Optimization (PPO), combining PPO’s exploration capabilities with GAIL’s demonstration learning. Expert demonstrations are generated using PD controller-based MMG dynamics in wave conditions, while the reinforcement learning environment utilizes Nomoto dynamics in calm water. Comparative analysis against standalone PPO and GAIL implementations across various path-following scenarios demonstrates that the hybrid policy achieves comparable path-following accuracy while significantly reducing the rudder actuation. The results indicate that this hybrid approach is particularly effective for real-world applications where control surface efficiency is crucial and high-quality expert demonstrations are limited.
Akula et al. (Sun,) studied this question.