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	<title>Artificial Intelligence Blog &#187; Artificial Intelligence Blog &#187; Control Systems</title>
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		<title>Reinforcement Learning for Linear Control Systems</title>
		<link>http://artent.net/2013/01/02/reinforcement-learning-for-linear-control-systems/</link>
		<comments>http://artent.net/2013/01/02/reinforcement-learning-for-linear-control-systems/#comments</comments>
		<pubDate>Wed, 02 Jan 2013 18:34:32 +0000</pubDate>
		<dc:creator><![CDATA[hundalhh]]></dc:creator>
				<category><![CDATA[Control Systems]]></category>
		<category><![CDATA[Reinforcement Learning]]></category>

		<guid isPermaLink="false">http://162.243.213.31/?p=1269</guid>
		<description><![CDATA[In &#8220;Efﬁcient Reinforcement Learning for High Dimensional Linear Quadratic Systems&#8221; Ibrahimi, Javanmard, and Van Roy (2012) present a fast reinforcement learning solution for linear control systems problems with quadratic costs.  The problem is to minimize the total cost $\min_u \sum_{i=1}^T x_i^t Q x_i + u_i^t R u_i$ for the control system $x_{i+1} = A x_i + B [&#8230;]]]></description>
				<content:encoded><![CDATA[<p>In &#8220;<a href="http://books.nips.cc/papers/files/nips25/NIPS2012_1237.pdf">Efﬁcient Reinforcement Learning for High Dimensional Linear Quadratic Systems</a>&#8221; Ibrahimi, Javanmard, and Van Roy (2012) present a fast <a href="http://en.wikipedia.org/wiki/Reinforcement_learning">reinforcement learning</a> solution for <a href="http://en.wikipedia.org/wiki/State_space_(controls)">linear control systems</a> problems with quadratic costs.  The problem is to minimize the total cost $\min_u \sum_{i=1}^T x_i^t Q x_i + u_i^t R u_i$ for the control system $x_{i+1} = A x_i + B u_i + w_i$ where $x\in R^j$ is the state of the system; $u\in R^k$ is the control; $Q, R, A,$ and $B$ are matrices; and $w_i\in R^j$ is a random multivariate normally distributed vector.</p>
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