Reinforcement Learning, an Introduction and Some Results

3 avr. 2024, 10:00
50m
Centre de Conférences Marilyn et James Simons (Le Bois-Marie)

Centre de Conférences Marilyn et James Simons

Le Bois-Marie

35, route de Chartres CS 40001 91893 Bures-sur-Yvette Cedex

Orateur

Erwan Le Pennec (CMAP, École polytechnique, Institut Polytechnique de Paris)

Description

Reinforcement Learning is the "art" of learning how to act in an environment that is only observed through interactions.
In this talk, I will provide an introduction to this topic starting from the underlying probabilistic model, Markov Decision Process, describing how to learn a good policy (how to pick the actions) when this model is known and when it is unknown. I will stress the impact of the (required) parametrization of the solution, as well as the importance of understanding the inner engine (stochastic approximation).
I will illustrate the variety of questions by describing briefly three different questions:
- How to apply Reinforcement Learning to detect faster an issue during an ultrasound exam ?
- How to solve faster an MDP using better approximation ?
- How to make RL more robust while controlling its sample complexity ?

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