%0 Journal Article
%T Stochastic Switching Model and Policy Optimization Online for Dynamic Power Management<br>动态电源管理的随机切换模型与在线优化
%A JIANG Qi
%A XI Hong-Sheng
%A YIN Bao-Qun
%A <br>江琦
%A 奚宏生
%A 殷保群
%J 自动化学报
%D 2007
%I 
%X 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 gr.,adient 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.
%K Dynamic power management
%K Markov decision processes
%K reinforcement learning
%K gradient estimation
%K stochastic approximation
%K on-line optimization<br>动态电源管理
%K Markov决策过程
%K 强化学习
%K 梯度估计
%K 随机逼近
%K 在线优化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=B9A2C62993075C85&yid=A732AF04DDA03BB3&vid=27746BCEEE58E9DC&iid=CA4FD0336C81A37A&sid=5C3443B19473A746&eid=4F2F18DD6F870C2C&journal_id=0254-4156&journal_name=自动化学报&referenced_num=3&reference_num=15