Improving Deep Q Network Based on Marketing Psychology for AUV Path Planning in Unknown Marine Environments

Autonomous underwater vehicles (AUVs) are commonly utilized in fields like marine environmental testing because of their adaptable and independent characteristics. However, the complexity and variability of the underwater environment present challenges for AUVs. AUVs need to safely and stably navigate through complex unknown underwater environments. The existing path planning technologies for AUVs in unknown marine environments face challenges, such as slow convergence speed and low success rate. In this article, a deep Q-learning (DQN) approach grounded in marketing psychology (MDQN) is suggested to tackle the challenges mentioned above. First, to reasonably allocate the exploration and exploitation abilities in DQN, a mathematical optimizer acceleration function is developed. Additionally, to improve the environmental adaptability of AUVs and speed up the convergence of deep networks, a reward mechanism that takes into account ocean currents and the shortest path principle is suggested. Finally, the reward feedback and experience stratification mechanism are designed based on marketing psychology principles, aiming to promote a virtuous cycle between the training process of deep networks and the learning process of the agent. Various simulation experiments comprehensively demonstrate that MDQN has excellent path planning capabilities in unknown marine environments, effectively addressing the slow convergence speed and low success rate issues of DQN.