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	<event:Event rdf:about="http://ebiquity.umbc.edu/event/html/id/235/Using-Expert-Traces-to-Reduce-Training-Time-in-Reinforcement-Learning">
		<rdfs:label><![CDATA[Using Expert Traces to Reduce Training Time in Reinforcement Learning]]></rdfs:label>
		<event:title><![CDATA[Using Expert Traces to Reduce Training Time in Reinforcement Learning]]></event:title>
		<event:speaker>
<person:MSAlumnus rdf:about="http://ebiquity.umbc.edu/person/html/Joel/Goldfinger"><person:name><![CDATA[Joel Goldfinger]]></person:name><rdfs:label><![CDATA[Joel Goldfinger]]></rdfs:label></person:MSAlumnus>
		</event:speaker>
		<event:startDate rdf:datatype="&xsd;dateTime">2008-04-21T11:15:00-05:00</event:startDate>
		<event:location><![CDATA[325b ITE]]></event:location>
		<event:abstract><![CDATA[Reinforcement learning algorithms are typically used to solve Markov
decision problems.  Using reinforcement learning to find an optimal or
sub-optimal solution is a slow process.  The use of prior knowledge can
shorten the length of this task.  In this thesis, we explore the use of
expert traces to speed up learning.  An expert trace is a record of the
states visited and actions taken by an expert in a given domain, which is
then used to reduce the training time of reinforcement learning.  We
demonstrate in a variety of domains significant improvements in learning
when complementing reinforcement learning with expert traces.
]]></event:abstract>
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<person:Collaborator rdf:about="http://ebiquity.umbc.edu/person/html/Tim/Oates"><person:name><![CDATA[Tim Oates]]></person:name><rdfs:label><![CDATA[Tim Oates]]></rdfs:label></person:Collaborator>
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