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A Reinforcement Learning Agent for Dynamic Power Management in Embedded Systems
Abstract
The use of effective power management strategies is essential in improving the power utilization efficiency and battery endurance of embedded systems. Accordingly, this paper presents a dynamic power management mechanism based on a reinforcement learning agent to adaptively manage the power consumption and service achievability of an embedded system device. The simulation results show that for a given set of environmental conditions, the proposed mechanism yields a notable improvement in both the power utilization efficiency and the battery endurance of the embedded system compared to that obtained using a static power management scheme.
Keywords
reinforcement learning; autonomous agent; dynamic power management
Citation Format:
Roy Chaoming Hsu, Cheng-Ting Liu, "A Reinforcement Learning Agent for Dynamic Power Management in Embedded Systems," Journal of Internet Technology, vol. 9, no. 4 , pp. 347-353, Oct. 2008.
Roy Chaoming Hsu, Cheng-Ting Liu, "A Reinforcement Learning Agent for Dynamic Power Management in Embedded Systems," Journal of Internet Technology, vol. 9, no. 4 , pp. 347-353, Oct. 2008.
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Published by Executive Committee, Taiwan Academic Network, Ministry of Education, Taipei, Taiwan, R.O.C
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