Reinforcement Learning in Continuous Time and Space: Interference and Not Ill Conditioning Is the Main Problem When Using Distributed Function Approximators. | D2R Server publishing the DBLP Bibliography Database, hosted at L3S Research Center

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dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Bart_Baddeley>
foaf:homepage	<http://dx.doi.org/doi.org%2F10.1109%2FTSMCB.2008.921000>
foaf:homepage	<https://doi.org/10.1109/TSMCB.2008.921000>
dc:identifier	DBLP journals/tsmc/Baddeley08 (xsd:string)
dc:identifier	DOI doi.org%2F10.1109%2FTSMCB.2008.921000 (xsd:string)
dcterms:issued	2008 (xsd:gYear)
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rdfs:label	Reinforcement Learning in Continuous Time and Space: Interference and Not Ill Conditioning Is the Main Problem When Using Distributed Function Approximators. (xsd:string)
foaf:maker	<https://dblp.l3s.de/d2r/resource/authors/Bart_Baddeley>
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swrc:pages	950-956 (xsd:string)
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rdfs:seeAlso	<http://dblp.uni-trier.de/db/journals/tsmc/tsmcb38.html#Baddeley08>
rdfs:seeAlso	<https://doi.org/10.1109/TSMCB.2008.921000>
dc:title	Reinforcement Learning in Continuous Time and Space: Interference and Not Ill Conditioning Is the Main Problem When Using Distributed Function Approximators. (xsd:string)
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