Publisher DOI: 10.1109/ICRA.2018.8461137
Title: Reinforcement learning of depth stabilization with a micro diving agent
Authors: Brinkmann, Gerrit 
Bessa, Wallace Moreira 
Dücker, Daniel-André 
Kreuzer, Edwin 
Solowjow, Eugen 
Issue Date: 10-Sep-2018
Source: Proceedings - IEEE International Conference on Robotics and Automation : 6197-6203 (2018-09-10)
Journal or Series Name: Proceedings - IEEE International Conference on Robotics and Automation 
Abstract (english): 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.
URI: http://hdl.handle.net/11420/2691
ISBN: 978-153863081-5
ISSN: 1050-4729
Institute: Mechanik und Meerestechnik M-13 
Type: InProceedings (Aufsatz / Paper einer Konferenz etc.)
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