Planning by Incremental Dynamic Programming

Planning by Incremental Dynamic Programming (1991)

by Richard S. Sutton

Venue:	In Proceedings of the Eighth International Workshop on Machine Learning
Citations:	53 - 2 self

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Datum	Value	Source
TITLE	Planning by Incremental Dynamic Programming	user correction - Legacy Corrections
AUTHOR NAME	Richard S. Sutton	SVM HeaderParse 0.1
AUTHOR AFFIL	GTE Laboratories Incorporated	SVM HeaderParse 0.2
AUTHOR ADDR	Waltham, MA 02254	SVM HeaderParse 0.1
ABSTRACT	This paper presents the basic results and ideas of dynamic programming as they relate most directly to the concerns of planning in AI. These form the theoretical basis for the incremental planning methods used in the integrated architecture Dyna. These incremental planning methods are based on continually updating an evaluation function and the situation-action mapping of a reactive system. Actions are generated by the reactive system and thus involve minimal delay, while the incremental planning process guarantees that the actions and evaluation function will eventually be optimal -- no matter how extensive a search is required. These methods are well suited to stochastic tasks and to tasks in which a complete and accurate model is not available. For tasks too large to implement the situation-action mapping as a table, supervised-learning methods must be used, and their capabilities remain a significant limitation of the approach.	user correction - Legacy Corrections
YEAR	1991	INFERENCE
VENUE	In Proceedings of the Eighth International Workshop on Machine Learning	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	353--357	INFERENCE
CITATIONS	22 found	ParsCit 1.0