Curious model-building control systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

406 Scopus citations

Abstract

A novel curious model-building control system is described which actively tries to provoke situations for which it learned to expect to learn something about the environment. Such a system has been implemented as a four-network system based on Watkins' Q-learning algorithm which can be used to maximize the expectation of the temporal derivative of the adaptive assumed reliability of future predictions. An experiment with an artificial nondeterministic environment demonstrates that the system can be superior to previous model-building control systems, which do not address the problem of modeling the reliability of the world model's predictions in uncertain environments and use ad-hoc methods (like random search) to train the world model.
Original languageEnglish (US)
Title of host publication1991 IEEE International Joint Conference on Neural Networks - IJCNN '91
PublisherPubl by IEEEPiscataway
Pages1458-1463
Number of pages6
ISBN (Print)0780302273
DOIs
StatePublished - Jan 1 1991
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

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