Cours

CS-456: Deep reinforcement learning

Résumé

This course provides an overview and introduces modern methods for reinforcement learning (RL.) The course starts with the fundamentals of RL, such as Q-learning, and delves into commonly used approaches, like PPO and DQN. The course will introduce students to practical applications of RL.

Séances de cours

Enseignant·e

Wulfram Gerstner

Wulfram Gerstner is Director of the Laboratory of Computational Neuroscience LCN at the EPFL. His research in computational neuroscience concentrates on models of spiking neurons and spike-timing dependent plasticity, on the problem of neuronal coding in single neurons and populations, as well as on the link between biologically plausible learning rules and behavioral manifestations of learning. He teaches courses for Physicists, Computer Scientists, Mathematicians, and Life Scientists at the EPFL. After studies of Physics in Tübingen and at the Ludwig-Maximilians-University Munich (Master 1989), Wulfram Gerstner spent a year as a visiting researcher in Berkeley. He received his PhD in theoretical physics from the Technical University Munich in 1993 with a thesis on associative memory and dynamics in networks of spiking neurons. After short postdoctoral stays at Brandeis University and the Technical University of Munich, he joined the EPFL in 1996 as assistant professor. Promoted to Associate Professor with tenure in February 2001, he is since August 2006 a full professor with double appointment in the School of Computer and Communication Sciences and the School of Life Sciences. Wulfram Gerstner has been invited speaker at numerous international conferences and workshops. He has served on the editorial board of the Journal of Neuroscience, Network: Computation in Neural Systems', Journal of Computational Neuroscience', and `Science'.

Source officielle

Page Moodle

http://go.epfl.ch/CS-456

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Résumé

Séances de cours

Enseignant·e

Wulfram Gerstner

Source officielle

Page Moodle

http://go.epfl.ch/CS-456

Séances de ce cours (96)

Introduction générale aux réseaux de neurones artificiels

Couvre l'histoire et l'inspiration derrière les réseaux neuronaux artificiels, la structure des neurones, l'apprentissage par les connexions synaptiques et la description mathématique des neurones artificiels.

Introduction générale aux réseaux de neurones artificiels: classification des images

Introduit des réseaux de neurones artificiels pour la classification d'images en utilisant des réseaux feedforward et récurrents en couches.

Introduction générale aux réseaux de neurones artificiels: partie 3

Couvre l'apprentissage par des récompenses dans l'apprentissage par renforcement profond sans détails mathématiques.

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