Objectif du cours
This class aims at providing a comprehensive and modern introduction to reinforcement learning concepts and algorithms. It endeavors to provide a solid formal basis on foundational notions of reinforcement learning.
Organisation des séances
8 lectures + 1 written exam
Lecture 1 : MDP and Dynamic Programming
Lecture 2 : The first algorithms of Reinforcement Learning
Lecture 3 : The Actors : Policy Iteration and Gradient
Lecture 4 : The Critics : RL with functiun Approximation
Lecture 5 : Scaling up RL : Towards Deep RL and Continuous MDPS
Lecture 6 : Exploration-Exploitation in RL
Lecture 7 : Average-reward Reinforcement Learning and Instance Optimal Algorithms
Lecture 8 : Bandit Methods for Pure Exploration and Planning in RL
Mode de validation
1 written exam (after class 8) and 1 programming assignment (after class 5)
Debabrota BASU
INRIA
Émilie KAUFMANN
INRIA, Université de Lille
Odalric MAILLARD
INRIA