Ship path planning based on improved DDPG algorithm in complex marine environment

To 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.

A 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.

Simulation 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.

The 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.