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EE-568: Reinforcement learning

Résumé

This course describes theory and methods for Reinforcement Learning (RL), which revolves around decision making under uncertainty. The course covers classic algorithms in RL as well as recent algorithms under the lens of contemporary optimization.

Séances de cours

Enseignant·e

Volkan Cevher

Volkan Cevher received the B.Sc. (valedictorian) in electrical engineering from Bilkent University in Ankara, Turkey, in 1999 and the Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in Atlanta, GA in 2005. He was a Research Scientist with the University of Maryland, College Park from 2006-2007 and also with Rice University in Houston, TX, from 2008-2009. Currently, he is an Associate Professor at the Swiss Federal Institute of Technology Lausanne and a Faculty Fellow in the Electrical and Computer Engineering Department at Rice University. His research interests include machine learning, signal processing theory, optimization theory and methods, and information theory. Dr. Cevher is an ELLIS fellow and was the recipient of the Google Faculty Research award in 2018, the IEEE Signal Processing Society Best Paper Award in 2016, a Best Paper Award at CAMSAP in 2015, a Best Paper Award at SPARS in 2009, and an ERC CG in 2016 as well as an ERC StG in 2011.

Source officielle

https://edu.epfl.ch/coursebook/fr/reinforcement-learning-EE-568

Page Moodle

http://go.epfl.ch/EE-568

Résumé

Séances de cours

Enseignant·e

Volkan Cevher

Source officielle

https://edu.epfl.ch/coursebook/fr/reinforcement-learning-EE-568

Page Moodle

http://go.epfl.ch/EE-568

Séances de ce cours (8)

Techniques d'apprentissage par renforcement profondes et robustes Connectez-vous à Mediaspace pour regarder cette vidéo

Discute des techniques avancées d'apprentissage par renforcement, en se concentrant sur des méthodes profondes et robustes, y compris des cadres d'acteur-critique et des stratégies d'apprentissage contradictoire.

Jeux de Markov: Concepts et applications dans l'apprentissage par renforcement Connectez-vous à Mediaspace pour regarder cette vidéo

Couvre les jeux de Markov, leur dynamique, leurs équilibres et leurs applications dans l'apprentissage par renforcement.

Apprentissage par renforcement : politiques non-stationnaires et OPPO Connectez-vous à Mediaspace pour regarder cette vidéo

Couvre l’apprentissage par renforcement d’horizons finis, les politiques non stationnaires et la variante optimiste de l’Optimisation Proximale des Politiques (OPPO).

Méthodes de gradient de politique: convergence et optimisation Connectez-vous à Mediaspace pour regarder cette vidéo

Couvre la convergence des méthodes de gradient de politique et leur optimisation dans l'apprentissage de renforcement.

Méthodes de gradient de politique dans l'apprentissage par renforcement Connectez-vous à Mediaspace pour regarder cette vidéo

Couvre les méthodes de gradient de politique dans l'apprentissage du renforcement, en se concentrant sur les techniques d'optimisation et les applications pratiques comme le problème du poteau.

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