Self-Generating Reward Network for AUV Path Planning With Hybrid Global-Local Optimization.
TL;DR
A novel AUV path planning framework incorporating generative adversarial imitation learning (GAIL) and DRL algorithm that automates reward function synthesis through adversarial learning from expert demonstrations is presented.
Abstract
Existing deep reinforcement learning (DRL) methods for autonomous underwater vehicle (AUV) path planning face two practical challenges: 1) dependency on manual reward engineering and 2) hyperparameter sensitivity in dynamic marine environments. This article presents a novel AUV path planning framework incorporating generative adversarial imitation learning (GAIL) and DRL algorithm that automates reward function synthesis through adversarial learning from expert demonstrations. The proposed architecture introduces a hierarchical reward mechanism that concurrently optimizes global trajectory planning and local motion constraints. By eliminating manual reward engineering, our approach reduces training complexity while maintaining policy convergence stability. Extensive experimentation demonstrates superior performance with 93.7% faster training convergence and 72.7% higher path convergence optimality compared to conventional DRL baselines. Two-tier validation confirms operational effectiveness: 1) Gazebo simulations achieve maximum 100% success rate in dynamic scenarios and 2) field deployments for submarine pipeline inspection attain 1 m average tracking accuracy. The results demonstrate that GAIL-DRL trained AUVs exhibit enhanced path planning stability while satisfying real-time planning requirements for marine transportation systems.