Policy Improvement for POMDPs Using Normalized Importance Sampling

We present a new method for estimating the expected return of a POMDP from experience. The estimator does not assume any knowledge of the POMDP, can estimate the returns for finite state controllers, allows experience to be gathered from arbitrary sequences of policies, and estimates the return for any new policy. We motivate the estimator from function-approximation and importance sampling points-of-view and derive its bias and variance. Although the estimator is biased, it has low variance and the bias is often irrelevant when the estimator is used for pair-wise comparisons. We conclude by extending the estimator to policies with memory and compare its performance in a greedy search algorithm to the REINFORCE algorithm showing an order of magnitude reduction in the number of trials required.

• Type: Conference
• Year: 2001
• Where: UAI
• Authors: Christian R. Shelton
• Keywords: Estimator, Finite State Controllers, Importance Sampling Points-of-view, UAI 2001, UAI 2008