[en] We consider the problem of decision making in the context of unknown Markov decision processes with finite state and action spaces. In a Bayesian reinforcement learning framework, we propose an optimistic posterior sampling strategy based on the maximization of state-action value functions of MDPs sampled from the posterior. First experiments are promising.
Disciplines :
Computer science
Author, co-author :
Fonteneau, Raphaël ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Korda, Nathan; University of Oxford, England
Munos, Rémi; Inria Lille - Nord Europe
Language :
English
Title :
An Optimistic Posterior Sampling Strategy for Bayesian Reinforcement Learning
Publication date :
2013
Event name :
NIPS 2013 Workshop on Bayesian Optimization (BayesOpt2013)
Event date :
10 décembre 2013
Main work title :
NIPS 2013 Workshop on Bayesian Optimization (BayesOpt2013)
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.