A Reinforcement-Learning Approach to Power Management

Title: en_US A Reinforcement-Learning Approach to Power Management
Creator: en_US Steinbach, Carl
Date: 2004-10-20T20:29:42Z
Date Available: 2004-10-20T20:29:42Z
Date Issued: en_US 2002-05-01
Identifier: en_US AITR-2002-007
uri: http://hdl.handle.net/1721.1/7093
Abstract: en_US 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.
Extent: en_US 41 p.; 8457203 bytes; 989455 bytes
Format: application/postscript; application/pdf
Language: en_US
Relation: en_US AITR-2002-007
Subject: en_US AI; en_US reinforcement learning; en_US power management; en_US wireless networks