Experiments with Infinite-Horizon, Policy-Gradient Estimation

Date

2001

Authors

Baxter, Jon
Bartlett, Peter
Weaver, L

Journal Title

Journal ISSN

Volume Title

Publisher

Morgan Kauffman Publishers

Abstract

In this paper, we present algorithms that perform gradient ascent of the average reward in a partially observable Markov decision process (POMDP). These algorithms are based on GPOMDP, an algorithm introduced in a companion paper (Baxter & Bartlett, 2001), which computes biased estimates of the performance gradient in POMDPs. The algorithm's chief advantages are that it uses only one free parameter β ∈ [0, 1), which has a natural interpretation in terms of bias-variance trade-off, it requires no knowledge of the underlying state, and it can be applied to infinite state, control and observation spaces. We show how the gradient estimates produced by GPOMDP can be used to perform gradient ascent, both with a traditional stochastic-gradient algorithm, and with an algorithm based on conjugate-gradients that utilizes gradient information to bracket maxima in line searches. Experimental results are presented illustrating both the theoretical results of Baxter and Bartlett (2001) on a toy problem, and practical aspects of the algorithms on a number of more realistic problems.

Description

Keywords

Citation

Source

Journal of Artificial Intelligence Research

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

DOI

Restricted until

2037-12-31