复杂海洋环境中基于改进DDPG算法的船舶智能路径规划OA

[Objective]To enhance ship path planning and obstacle avoidance in complex marine environ-ments while improving the efficiency and safety of ship navigation,this study proposes a novel method based on an improved DDPG algorithm.[Method]A priority experience replay mechanism,guided by a path im-portance score,is introduced to enhance the utilization efficiency of important experience in the learning pro-cess.A self-attention mechanism is integrated into the actor-critic network to enhance its ability to capture en-vironmental features.In addition,the network architecture is optimized by using the dueling deep Q-network to improve the accuracy of value function estimation.[Results]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.[Conclusion]The improved algorithm is superior to DDPG and traditional A* algorithms in marine environments of varying complexity,demonstrating its significant advan-tages and strong generalizability.It provides a reference for intelligent decision-making in ship navigation.