Bewegte Bilder
Model-free control and deep learning
In this talk I will present some recent developments in model-free reinforcement learning applied to large state spaces, with an emphasis on deep learning and its role in estimating action-value functions. The talk will cover a variety of model-free algorithms, including variations on Q-Learning, and some of the main techniques that make the approach practical. I will illustrate the usefulness of these methods with examples drawn from the Arcade Learning Environment, the popular set of Atari 2600 benchmark domains.
- Standort
-
Hannover TIB
- Umfang
-
1986MB, 01:04:34:00 (unknown)
- Sprache
-
Englisch
- Anmerkungen
-
Audiovisuelles Material
- Erschienen in
-
CEMRACS 2017 : Numerical Methods for Stochastic Models: Control, Uncertainty Quantification, Mean-field ; (Jan. 2017)
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre International de Rencontres Mathématiques (CIRM)
- (wann)
-
2017-01-01
- Beteiligte Personen und Organisationen
-
Bellemare, Marc
Hennenfent, Guillaume
Bouchard, Bruno
Delarue, François
Gobet, Emmanuel
Lelong, Jérôme
Chassagneux, Jean-François
- DOI
-
10.5446/57389
- Letzte Aktualisierung
- 21.04.2026, 10:50 MESZ
Datenpartner
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Objekttyp
- zweidimensionales bewegtes Bild
Beteiligte
- Bellemare, Marc
- Hennenfent, Guillaume
- Bouchard, Bruno
- Delarue, François
- Gobet, Emmanuel
- Lelong, Jérôme
- Chassagneux, Jean-François
- Centre International de Rencontres Mathématiques (CIRM)
Entstanden
- 2017-01-01
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