基于改进DQN算法的船舶全局路径规划研究

Research on ship global path planning based on improved DQN algorithm

  • 摘要:
    目的 为提升实际海域环境下船舶航行路径的经济性与安全性,提出一种改进深度Q网络(DQN)算法的船舶全局路径规划方法。
    方法 首先,引入优先经验回放机制赋予重要样本更高的权重,提升学习效率;然后,再通过决斗网络和噪声网络改进DQN的网络结构,使其对特定状态及其动作的价值评估更加准确,并同时具备一定的探索性和泛化性。
    结果 实验结果表明,在马尼拉附近海域环境下,相比于A*算法和DQN算法,改进算法在路径长度上分别缩短了1.9%和1.0%,拐点数量上分别减少了62.5%和25%。
    结论 实验结果验证了改进DQN算法能够更经济、更合理地规划出有效路径。

     

    Abstract:
    Objective In order to improve the economy and safety of ship navigation path in actual sea environment, this paper proposes a ship global path planning method with an improved Deep Q-Network (DQN) algorithm.
    Method First, a prioritized experience replay (PER) mechanism is introduced to the DQN to give higher weights to important samples and improve learning efficiency. Next, its network structure is improved through a duel network and noise network, enabling it to evaluate the values of specific states and actions more accurately and generalization capabilities.
    Result An experiment is carried out in the marine environment near Manila, and the results show that compared with the A* algorithm and DQN algorithm, the improved algorithm reduces the path length by 1.9% and 1.0% respectively, and the number of turning points by 62.5% and 25% respectively.
    Conclusion It is verified that the improved DQN algorithm can plan the effective path more economically and rationally.

     

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