A Reinforcement-Learning Approach to Power Management

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7093

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DC Field	Value	Language
dc.creator	Steinbach, Carl	-
dc.date	2004-10-20T20:29:42Z	-
dc.date	2004-10-20T20:29:42Z	-
dc.date	2002-05-01	-
dc.date.accessioned	2013-10-09T02:48:21Z	-
dc.date.available	2013-10-09T02:48:21Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AITR-2002-007	-
dc.identifier	http://hdl.handle.net/1721.1/7093	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	We describe an adaptive, mid-level approach to the wireless device power management problem. Our approach is based on reinforcement learning, a machine learning framework for autonomous agents. We describe how our framework can be applied to the power management problem in both infrastructure and ad~hoc wireless networks. From this thesis we conclude that mid-level power management policies can outperform low-level policies and are more convenient to implement than high-level policies. We also conclude that power management policies need to adapt to the user and network, and that a mid-level power management framework based on reinforcement learning fulfills these requirements.	-
dc.format	41 p.	-
dc.format	8457203 bytes	-
dc.format	989455 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AITR-2002-007	-
dc.subject	AI	-
dc.subject	reinforcement learning	-
dc.subject	power management	-
dc.subject	wireless networks	-
dc.title	A Reinforcement-Learning Approach to Power Management	-
Appears in Collections:	MIT Items

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