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dc.contributor.authorIsbell, Charles L.en_US
dc.date.accessioned2004-10-20T20:23:50Z
dc.date.available2004-10-20T20:23:50Z
dc.date.issued1992-12-01en_US
dc.identifier.otherAITR-1424en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7050
dc.description.abstractThere has been recent interest in using temporal difference learning methods to attack problems of prediction and control. While these algorithms have been brought to bear on many problems, they remain poorly understood. It is the purpose of this thesis to further explore these algorithms, presenting a framework for viewing them and raising a number of practical issues and exploring those issues in the context of several case studies. This includes applying the TD(lambda) algorithm to: 1) learning to play tic-tac-toe from the outcome of self-play and of play against a perfectly-playing opponent and 2) learning simple one-dimensional segmentation tasks.en_US
dc.format.extent223511 bytes
dc.format.extent456055 bytes
dc.format.mimetypeapplication/octet-stream
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesAITR-1424en_US
dc.titleExplorations of the Practical Issues of Learning Prediction-Control Tasks Using Temporal Difference Learning Methodsen_US


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