Wendelin Böhmer, Jost Tobias Springenberg, Joschka Boedecker, Martin Riedmiller, Klaus Obermayer (2015)
Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations.
KI - Künstliche Intelligenz pp. 1-10. Springer Berlin Heidelberg.
doi web Bibtex @article{BoeSprBoeRieObeKI2015,
year={2015},
issn={0933-1875},
journal={KI - Künstliche Intelligenz},
doi={10.1007/s13218-015-0356-1},
title={Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations},
url={http://dx.doi.org/10.1007/s13218-015-0356-1},
publisher={Springer Berlin Heidelberg},
keywords={End-to-end reinforcement learning; Representation learning; Deep auto-encoder networks; Slow feature analysis; Autonomous robotics; BoedeckerSelect},
author={Böhmer, Wendelin and Springenberg, Jost Tobias and Boedecker, Joschka and Riedmiller, Martin and Obermayer, Klaus},
pages={1-10}
}