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Accueil > Formations > Master MVA > Présentation des cours
Reinforcement learning
Lecturer : Matteo Pirotta (INRIA)
Objective of the course :
Introduction to the models and mathematical tools used in formalizing the problem of learning and decision-making under uncertainty. In particular, we will focus on the frameworks of reinforcement learning and multi-arm bandit.
Topics :
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Historical multi-disciplinary basis of reinforcement learning
Markov decision processes and dynamic programming
Stochastic approximation and Monte-Carlo methods
Function approximation and statistical learning theory
Approximate dynamic programming
Introduction to stochastic and adversarial multi-arm bandit
Learning rates and finite-sample analysis
Prerequisites :
Basic of Probability and Statistics (niveau L3 maths ou GE)
Organization of courses :
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8 cours théoriques de 2h
3 travaux dirigés de 3h
Validation :
Reading of papers of interest, implementation or theoretical analysis of reinforcement learning algorithms. The project will be evaluated on the basis of a short report and an oral presentation.
References :
- Sutton, R. et Barto, A. Reinforcement Learning: An Introduction.
- Processus decisionnels de Markov et Intelligence Artificielle, 2008. Editeurs O. Sigaud et O. Buffet.
- Neuro-Dynamic Programming, Bertsekas et Tsitsiklis, 1996.
- Algorithms for Reinforcement Learning. Cs. Szepesvari, 2009