BErMin: A Model Selection Algorithm for Reinforcement Learning Problems
published: Jan. 25, 2012, recorded: December 2011, views: 4062
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
We consider the problem of model selection in the batch (offline, non-interactive) reinforcement learning setting when the goal is to find an action-value function with the smallest Bellman error among a countable set of candidate functions. We propose a complexity regularization-based model selection algorithm, BErMin, and prove that it enjoys an oracle-like property: the estimator's error differs from that of an oracle, who selects the candidate with the minimum Bellman error, by only a constant factor and a small remainder term that vanishes at a parametric rate as the number of samples increases.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !