Inproceedings,
PAC-Bayesian Policy Evaluation for Reinforcement Learning
UAI, page 195--202. (July 2011)
Abstract
Bayesian priors offer a compact yet general means of incorporating domain knowledge into many learning tasks. The correctness of the Bayesian analysis and inference, however, largely depends on accuracy and correctness of these priors. PAC-Bayesian methods over- come this problem by providing bounds that hold regardless of the correctness of the prior distribution. This paper introduces the first PAC-Bayesian bound for the batch reinforce- ment learning problem with function approx- imation. We show how this bound can be used to perform model-selection in a trans- fer learning scenario. Our empirical results confirm that PAC-Bayesian policy evaluation is able to leverage prior distributions when they are informative and, unlike standard Bayesian RL approaches, ignore them when they are misleading.
