Towards more practical reinforcement learning

Qualtrough, Paul

dc.contributor.author	Qualtrough, Paul	en
dc.date.accessioned	2009-06-16T01:52:44Z	en
dc.date.available	2009-06-16T01:52:44Z	en
dc.date.issued	1997	en
dc.identifier.citation	Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Orlando, FL, USA, 2770-2775. (1997)	en
dc.identifier.issn	08843627	en
dc.identifier.uri	http://hdl.handle.net/2292/4388	en
dc.description	An open access copy of this article is available and complies with the copyright holder/publisher conditions.	en
dc.description.abstract	The fields of machine learning, mobile robotics and machine vision have grown steadily closer in recent years, to the extent that learning has been suggested as the best means of producing sophisticated controllers for mobile robots. Such an approach may have merit, but only if the structures and mechanisms provided for learning are tuned to the special needs of robots. These needs are outlined, and reinforcement learning is promoted as the best starting point for fulfilling them. In order to make good on the promise of learning to the level required of mobile robots, significant enhancements are required to current formulations of reinforcement learning. The issues involved in making improvements are discussed, and a simple enhanced model of reinforcement learning is suggested as a first step in this direction	en
dc.publisher	IEEE	en
dc.relation.ispartof	Proceedings of the IEEE International Conference on Systems, Man and Cybernetics	en
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dc.rights.uri	https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm	en
dc.title	Towards more practical reinforcement learning	en
dc.type	Conference Paper	en
dc.subject.marsden	Fields of Research::290000 Engineering and Technology	en
dc.identifier.doi	10.1109/ICSMC.1997.635368	en
pubs.begin-page	2770-2775.	en
dc.description.version	VoR - Version of Record	en
dc.rights.holder	Copyright IEEE	en
dc.rights.accessrights	http://purl.org/eprint/accessRights/OpenAccess	en