|Publisher DOI:||10.1109/ICRA.2018.8461137||Title:||Reinforcement learning of depth stabilization with a micro diving agent||Authors:||Brinkmann, Gerrit
Bessa, Wallace Moreira
|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:||Sonstiges|
|Appears in Collections:||Publications without fulltext|
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