@article{LIU2026, 
author = {Shen LIU and Deqing YANG},
title = {Ship path planning based on improved DDPG algorithm in complex marine environment},
year = {2026},
journal = {Chinese Journal of Ship Research},
volume = {21},
number = {3},
pages = {337-345},
keywords = {obstacle avoidance, A* algorithm, route planning, ships, DDPG algorithm},
url = {https://www.sciopen.com/article/10.19693/j.issn.1673-3185.04362},
doi = {10.19693/j.issn.1673-3185.04362},
abstract = {ObjectiveTo enhance ship path planning and obstacle avoidance in complex marine environments while improving the efficiency and safety of ship navigation, this study proposes a novel method based on an improved DDPG algorithm.MethodA priority experience replay mechanism, guided by a path importance score, is introduced to enhance the utilization efficiency of important experience in the learning process. A self-attention mechanism is integrated into the actor-critic network to enhance its ability to capture environmental features. In addition, the network architecture is optimized by using the dueling deep Q-network to improve the accuracy of value function estimation. ResultsSimulation results in the East China Sea and the Indian Ocean show that, compared with the DDPG and A* algorithms, the improved algorithm achieves significant improvements in path length, inflection points and collision avoidance. For example, in the East China Sea, the improved algorithm reduces path length by 0.75%, inflection points by 26.92%, and collisions by 15.80% compared with the DDPG algorithm; and reduces path length by 4.59% and inflection points by 42.42% compared with the A* algorithm. ConclusionThe improved algorithm is superior to DDPG and traditional A* algorithms in marine environments of varying complexity, demonstrating its significant advantages and strong generalizability. It provides a reference for intelligent decision-making in ship navigation.}
}