  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.
