Associative Reinforcement Learning: Functions in k-DNF

Associative Reinforcement Learning: Functions in k-DNF (1994)

by Leslie Pack Kaelbling

Venue:	Machine Learning
Citations:	5 - 0 self

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Datum	Value	Source
TITLE	Associative Reinforcement Learning: Functions in k-DNF	user correction - Legacy Corrections
AUTHOR NAME	Leslie Pack Kaelbling	SVM HeaderParse 0.1
AUTHOR AFFIL	; Computer Science Department; Brown University	SVM HeaderParse 0.2
AUTHOR ADDR	; Box 1910; Providence, RI 02912-1910 USA	SVM HeaderParse 0.1
ABSTRACT	An agent that must learn to act in the world by trial and error faces the reinforcement learning problem, which is quite different from standard concept learning. Although good algorithms exist for this problem in the general case, they are often quite inefficient and do not exhibit generalization. One strategy is to find restricted classes of action policies that can be learned more efficiently. This paper pursues that strategy by developing algorithms that can efficiently learn action maps that are expressible in k-DNF. The algorithms are compared with existing methods in empirical trials and are shown to have very good performance.	user correction - Legacy Corrections
YEAR	1994	INFERENCE
VENUE	Machine Learning	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	298	INFERENCE
VOLUME	15	INFERENCE
CITATIONS	23 found	ParsCit 1.0