Reinforcement Learning of Depth Stabilization with a Micro Diving Agent

: Brinkmann Gerrit, Bessa Wallace M., Duecker Daniel A., Kreuzer Edwin, Solowjow Eugen

: Kevin Lynch

: IEEE International Conference on Robotics and Automation

: 2018

IEEE International Conference on Robotics and Automation

: Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA)

: IEEE International Conference on Robotics and Automation

: 6197

: 6203

: 978-1-5386-3082-2

: 978-1-5386-3081-5

: 2152-4092

DOI: https://doi.org/10.1109/ICRA.2018.8461137

: https://ieeexplore.ieee.org/document/8461137

Reinforcement learning (RL) allows robots to solve control tasks through interaction with their environment. In this paper we study a model-based value-function RL approach, which is suitable for computationally limited robots and light embedded systems. We develop a diving agent, which uses the RL algorithm for underwater depth stabilization. Simulations and experiments with the micro diving agent demonstrate its ability to learn the depth stabilization task.

neural networks, reinforcement learning, Robotics and automation