@COMMENT This file was generated by bib2html.pl version 0.90
@COMMENT written by Patrick Riley
@COMMENT This file came from Peter Stone's publication pages at
@COMMENT http://www.cs.utexas.edu/~pstone/papers
@InProceedings{AAMAS07-jong,
author="Nicholas K. Jong and Peter Stone",
title="Model-Based Function Approximation for Reinforcement Learning",
booktitle="The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems",
month="May",year="2007",
abstract={
Reinforcement learning promises a generic method for
adapting agents to arbitrary tasks in arbitrary
stochastic environments, but applying it to new
real-world problems remains difficult, a few
impressive success stories notwithstanding. Most
interesting agent-environment systems have large
state spaces, so performance depends crucially on
efficient generalization from a small amount of
experience. Current algorithms rely on model-free
function approximation, which estimates the long-term
values of states and actions directly from data and
assumes that actions have similar values in similar
states. This paper proposes model-based function
approximation, which combines two forms of
generalization by assuming that in addition to having
similar values in similar states, actions also have
similar effects. For one family of generalization
schemes known as averagers, computation of an
approximate value function from an approximate model
is shown to be equivalent to the computation of the
exact value function for a finite model derived from
data. This derivation both integrates two
independent sources of generalization and permits the
extension of model-based techniques developed for
finite problems. Preliminary experiments with a
novel algorithm, AMBI (Approximate Models Based on
Instances), demonstrate that this approach yields
faster learning on some standard benchmark problems
than many contemporary algorithms.
},
}