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

PSC In The News

RSS Feed icon

Ela and Budnick find higher unintended pregnancy risk among non-heterosexual women

Trends in frequent adolescent binge drinking, 1991-2015

Detroit Mayor challenges U-M to analyze root causes, patterns of murders in city

More News

Highlights

Bailey, Eisenberg , and Fomby promoted at PSC

Former PSC trainee Eric Chyn wins PAA's Dorothy S. Thomas Award for best paper

Celebrating departing PSC trainees

Bloome finds children raised outside stable 2-parent families more likely to become low-income adults, regardless of parents' income

More Highlights

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