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Model-free control and deep learning

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Authors : Bellemare, Marc (Author of the conference)
CIRM (Publisher )

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Abstract : 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.

MSC Codes :
68Q32 - Computational learning theory
68T05 - Learning and adaptive systems
91A25 - Dynamic games

Additional resources :
http://smai.emath.fr/cemracs/cemracs17/Slides/bellemare.pdf

    Information on the Video

    Film maker : Hennenfent, Guillaume
    Language : English
    Available date : 26/07/17
    Conference Date : 19/07/17
    Subseries : Research School
    arXiv category : Machine Learning ; Computer Vision and Pattern Recognition
    Mathematical Area(s) : Computer Science
    Format : MP4 (.mp4) - HD
    Video Time : 01:04:34
    Targeted Audience : Researchers ; Graduate Students
    Download : https://videos.cirm-math.fr/2017-07-19_Bellemare.mp4

Information on the Event

Event Title : CEMRACS - Summer school: Numerical methods for stochastic models: control, uncertainty quantification, mean-field / CEMRACS - École d'été : Méthodes numériques pour équations stochastiques : contrôle, incertitude, champ moyen
Event Organizers : Bouchard, Bruno ; Chassagneux, Jean-François ; Delarue, François ; Gobet, Emmanuel ; Lelong, Jérôme
Dates : 17/07/17 - 25/08/17
Event Year : 2017
Event URL : http://conferences.cirm-math.fr/1556.html

Citation Data

DOI : 10.24350/CIRM.V.19199703
Cite this video as: Bellemare, Marc (2017). Model-free control and deep learning. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19199703
URI : http://dx.doi.org/10.24350/CIRM.V.19199703

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