Stochastic switching model and policy optimization online for dynamic power management

被引：2

作者：

Jiang, Qi ^{[1
]}

Xi, Hong-Sheng ^{[1
]}

Yin, Bao-Qun ^{[1
]}

机构：

[1] Department of Automation, University of Science and Technology of China, Hefei 230027, China

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2007年 / 33卷 / 01期

关键词：

Computer simulation - Markov processes - Optimization - Reinforcement learning;

D O I：

10.1360/aas-007-0066

中图分类号：

学科分类号：

摘要：

A reinforcement learning based online optimization algorithm is presented for dynamic power management with unknown system parameters. First an event-driven stochastic switching model is introduced to formulate dynamic power management problem as a constrained policy optimization problem. Then by utilizing the features of this model an online optimization algorithm that combines policy gradient estimation and stochastic approximation is derived. The stochastic switching model captures the power-managed system behaves accurately. The optimization algorithm is adaptive, and can achieve global optimum with less computational cost. Simulation results demonstrate the effectiveness of the proposed approach.

引用

页码：66 / 71

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