An Offline Reinforcement Learning Approach for Path Following of an Unmanned Surface Vehicle
Year: 2024
Type: article
Abstract: Path following is crucial for enhancing the autonomy of unmanned surface vehicles (USVs) in water monitoring missions. This paper presents an offline reinforcement learning (RL) controller for USVs. The controller employs the soft actor–critic algorithm with a diversified Q-ensemble to optimize the steering control policy using a pre-collected dataset of USV path-following trials. A Markov decision process (MDP) tailored for path following is formulated. The proposed offline RL steering controller, trained on static datasets, demonstrates improved sample efficiency and asymptotic performance due to an expanded ensemble of Q-networks. The accuracy and adaptive learning capabilities of the RL controller are validated through simulations and free-running tests.
DOI: 10.3390/jmse12122173
Authors: Zexing Zhou, Tao Bao, Jun Ding, Yihong Chen, Zhengyi Jiang, Bo Zhang
Institution: China Ship Scientific Research Center
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,955
OpenAlex ID: W4404827480