State representation learning for control: An overview

Timothée Lesort, Natalia Díaz-Rodríguez, Jean-Frano̧is Goudou, David Filliat

Open source

DOI: 10.1016/j.neunet.2018.07.006
Published: 2018-12
Container: Neural Networks
Publisher: Elsevier BV
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1016/j.neunet.2018.07.006,
  title = {State representation learning for control: An overview},
  author = {Timothée Lesort and Natalia Díaz-Rodríguez and Jean-Frano̧is Goudou and David Filliat},
  year = {2018},
  journal = {Neural Networks},
  doi = {10.1016/j.neunet.2018.07.006},
  url = {https://doi.org/10.1016/j.neunet.2018.07.006}
}

RIS

TY  - JOUR
TI  - State representation learning for control: An overview
AU  - Timothée Lesort
AU  - Natalia Díaz-Rodríguez
AU  - Jean-Frano̧is Goudou
AU  - David Filliat
PY  - 2018
JO  - Neural Networks
DO  - 10.1016/j.neunet.2018.07.006
UR  - https://doi.org/10.1016/j.neunet.2018.07.006
ER  -

APA

Lesort, T., Díaz-Rodríguez, N., Goudou, J., & Filliat, D. (2018). State representation learning for control: An overview. Neural Networks. https://doi.org/10.1016/j.neunet.2018.07.006

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crossref · retrieved 2026-09-25T23:44:43.397Z