Deep Reinforcement Learning Algorithm in Intelligent Ship Autonomous Navigation System

With the acceleration of the intelligent transformation of the shipping industry, intelligent ships have become a research and application hotspot in this field. As a core component of intelligent ship technology, ship navigation control technology plays a crucial role in ensuring navigation safety and improving navigation efficiency. Among them, ship collision avoidance is particularly critical in complex and everchanging marine environments, especially in complex situations where multiple ships encounter each other. How to reduce collision risks through effective collision avoidance strategies has become the primary challenge that intelligent navigation technology urgently needs to solve. This article delves into the innovative application of collision avoidance strategies in intelligent ship autonomous navigation systems and proposes an autonomous navigation solution based on deep reinforcement learning (DRL). This solution utilizes the powerful adaptive learning and decision-making capabilities of DRL to continuously learn and optimize collision avoidance strategies in complex navigation environments by constructing intelligent agents. The experimental results show that the system can accurately perceive the dynamic environment, intelligently plan the optimal collision avoidance path, significantly reduce collision risk, and ensure the continuity and economy of navigation.