A selected review on reinforcement learning based control for autonomous underwater vehicles
This is an incremental review paper summarizing existing RL applications for AUV control, aimed at researchers in underwater robotics.
This paper reviews reinforcement learning (RL) based control methods for autonomous underwater vehicles (AUVs), focusing on low-level tasks like regulation and tracking, and discusses challenges, recent progress, and provides detailed case studies of model-free RL controllers.
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.