Intelligent collision avoidance decision-making method for ships based on Dueling DDQN
A ship intelligent collision avoidance decision mod-el based on deep reinforcement learning(DRL)algorithm was proposed to address the frequent collision accidents caused by the global increase in the number of ships at sea.The model was based on adversarial dual deep Q-learning(Dueling DDQN)and the establishment of ship domain models,and when designing the reward function,factors such as COL-REGs(International Regulations for Preventing Collisions at Sea)and ship deviation were fully considered to ensure the compliance and rationality of collision avoidance decisions.A simulation environment was constructed to simulate the sce-nario of multiple ships encountering,and neural network mod-els were used to process complex environmental information for model training and validation.Experimental results show that compared with traditional deep Q-learning algorithms,the model proposed in this paper exhibits significant advantages in convergence speed and stability,which can accurately deter-mine the encounter situation and take appropriate collision a-voidance measures based on COLREGs,demonstrating high decision accuracy and reliability.It can provide effective deci-sion support for intelligent navigation of ships in complex sea conditions.
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