Policy Evaluation with Stochastic Gradient Estimation Techniques

Abstract

In this paper, we consider policy evaluation in a finite-horizon setting with continuous state variables. The Bellman equation represents the value function as a conditional expectation, which can be further transformed into a ratio of two stochastic gradients. By using the finite difference method and the generalized likelihood ratio method, we propose new estimators for policy evaluation and show how the value of any given state can be estimated using sample paths starting from various other states.