Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Starr's findings account for some of the 19% black-white gap in federal sentencing

Frey says suburbs are aging, cities draw millennials

Pfeffer comments on Fed report that reveals 20-year decline in net worth among American families

More News

Highlights

U-M's campus climate survey results discussed in CHE story

U-M honors James Jackson's groundbreaking work on how race impacts the health of black Americans

U-M is the only public and non-coastal university on Forbes' top-10 list for billionaire production

ASA President Bonilla-Silva takes exception with Chief Justice Roberts' 'gobbledygook' jab

More Highlights

Next Brown Bag

Mon, Jan 22, 2018, noon: Narayan Sastry

Sampling strategies for batch mode reinforcement learning

Publication Abstract

Fonteneau, Raphael, Susan A. Murphy, L. Wehenkel, and D. Ernst. 2013. "Sampling strategies for batch mode reinforcement learning." Revue d'Intelligence Artificielle, 27(2): 171-194.

We propose two strategies for experiment selection in the context of batch mode reinforcement learning. The first strategy is based on the idea that the most interesting experiments to carry out at some stage are those that are the most liable to falsify the current hypothesis about the optimal control policy. We cast this idea in a context where a policy learning algorithm and a model identification method are given a priori. The second strategy exploits recently published methods for computing bounds on the return of control policies from a set of trajectories in order to sample the state-action space so as to be able to discriminate between optimal and non-optimal policies. Both strategies are experimentally validated, showing promising results. © 2013 Lavoisier.

DOI:10.3166/RIA.27.171-194 (Full Text)

Browse | Search : All Pubs | Next