Reinforcement learning of depth stabilization with a micro diving agent

Publikationstyp
Conference Paper
Date Issued
2018-09-10
Author(s)
Brinkmann, Gerrit  
Bessa, Wallace Moreira  
Dücker, Daniel-André 
Kreuzer, Edwin  
Solowjow, Eugen  
Institut
Mechanik und Meerestechnik M-13  
TORE-URI
http://hdl.handle.net/11420/2691
Start Page
6197
End Page
6203
Citation
IEEE International Conference on Robotics and Automation : 6197-6203 (2018-09-10)
Contribution to Conference
IEEE International Conference on Robotics and Automation, ICRA 2018  
Publisher DOI
10.1109/ICRA.2018.8461137
Scopus ID
2-s2.0-85060152374
Publisher
IEEE
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.
DDC Class
620: Ingenieurwissenschaften