Reinforcement Learning of Depth Stabilization with a Micro Diving Agent

Gerrit Brinkmann; Wallace M. Bessa; Daniel-A. Duecker; Edwin Kreuzer; Eugen Solowjow

ICRA 2018poster6 citations

Reinforcement Learning of Depth Stabilization with a Micro Diving Agent

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

Abstract

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.

BibTeX

@inproceedings{icra2018_reinforcementlea,
  title = {Reinforcement Learning of Depth Stabilization with a Micro Diving Agent},
  author = {Gerrit Brinkmann and Wallace M. Bessa and Daniel-A. Duecker and Edwin Kreuzer and Eugen Solowjow},
  booktitle = {ICRA 2018},
  year = {2018}
}