A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Reinforcement Learning of Depth Stabilization with a Micro Diving Agent
Tekijät: Brinkmann Gerrit, Bessa Wallace M., Duecker Daniel A., Kreuzer Edwin, Solowjow Eugen
Toimittaja: Kevin Lynch
Konferenssin vakiintunut nimi: IEEE International Conference on Robotics and Automation
Julkaisuvuosi: 2018
Journal: IEEE International Conference on Robotics and Automation
Kokoomateoksen nimi: Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA)
Sarjan nimi: IEEE International Conference on Robotics and Automation
Aloitussivu: 6197
Lopetussivu: 6203
ISBN: 978-1-5386-3082-2
eISBN: 978-1-5386-3081-5
ISSN: 2152-4092
DOI: https://doi.org/10.1109/ICRA.2018.8461137
Verkko-osoite: 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.
