Collection 2022 - T3 - WS1 - Non-Linear and High Dimensional Inference

Organizer(s) Aamari, Eddie ; Aaron, Catherine ; Chazal, Frédéric ; Fischer, Aurélie ; Hoffmann, Marc ; Le Brigant, Alice ; Levrard, Clément ; Michel, Bertrand

Date(s) 03/10/2022 - 07/10/2022

linked URL https://indico.math.cnrs.fr/event/7545/

10 21

Dimensionality reduction in reinforcement learning by randomisation

By Denis Belomestny

In reinforcement learning an agent interacts with an environment, whose underlying mechanism is unknown, by sequentially taking actions, receiving rewards, and transitioning to the next state. With the goal of maximizing the expected sum of the collected rewards, the agent must carefully balance between exploring in order to gather more information about the environment and exploiting the current knowledge to collect the rewards. In this talk, we are interested in solving this exploration-exploitation dilemma by injecting noise into the agent’s decision-making process in such a way that the dependence of the regret on the dimension of state and action spaces is minimised. We also review some recent approaches towards dimension reduction in RL.

Information about the video

Date of recording 06/10/2022
Date of publication 20/04/2024
Institution IHP
Language English
Audience Researchers, Graduate Students
Director(s) Alexandre Duplessis, Marco Perez
Format MP4
Venue Amphitheater Hermite, IHP

Citation data

DOI 10.57987/IHP.2022.T3.WS1.010
Cite this video Belomestny, Denis (06/10/2022). Dimensionality reduction in reinforcement learning by randomisation. IHP. Audiovisual resource. DOI: 10.57987/IHP.2022.T3.WS1.010
URL https://dx.doi.org/10.57987/IHP.2022.T3.WS1.010