A selected review on reinforcement learning based control for autonomous underwater vehicles

TL;DR

This paper reviews recent advances in reinforcement learning applications for controlling autonomous underwater vehicles, focusing on low-level control tasks like regulation and tracking, highlighting challenges, progress, and specific case studies.

Contribution

It provides a comprehensive overview of RL-based control methods for AUVs, including recent progress, challenges, and detailed case studies of model-free RL controllers.

Findings

RL methods improve AUV control capabilities

Recent progress addresses key control challenges

Case studies demonstrate effectiveness of model-free RL

Abstract

Recently, reinforcement learning (RL) has been extensively studied and achieved promising results in a wide range of control tasks. Meanwhile, autonomous underwater vehicle (AUV) is an important tool for executing complex and challenging underwater tasks. The advances in RL offers ample opportunities for developing intelligent AUVs. This paper provides a selected review on RL based control for AUVs with the focus on applications of RL to low-level control tasks for underwater regulation and tracking. To this end, we first present a concise introduction to the RL based control framework. Then, we provide an overview of RL methods for AUVs control problems, where the main challenges and recent progresses are discussed. Finally, two representative cases of RL-based controllers are given in detail for the model-free RL methods on AUVs.