A Reinforcement-Learning Approach to Power Management

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

Files in this item

Files	Size	Format	View
There are no files associated with this item.