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DTSTART:20190310T070000
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DTSTART;TZID=America/Toronto:20190816T143000
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URL:https://uwaterloo.ca/artificial-intelligence-group/events/masters-thesi
 s-presentation-policy-extraction-online-q-value
LOCATION:E7 - Engineering 7 200 University Ave West 5419 Waterloo ON N2L 3G
 1 Canada
SUMMARY:Master’s Thesis Presentation: Policy Extraction via Online Q-Valu
 e\nDistillation
CLASS:PUBLIC
DESCRIPTION:AMAN JHUNJHUNWALA\, MASTER’S CANDIDATE\n_David R. Cheriton Sc
 hool of Computer Science_\n\nRecently\, deep neural networks have been cap
 able of solving complex\ncontrol tasks in certain challenging environments
 . However\, these deep\nlearning policies continue to be hard to interpret
 \, explain and\nverify\, which limits their practical applicability. Decis
 ion Trees\nlend themselves well to explanation and verification tools but 
 are not\neasy to train especially in an online fashion. The aim of this th
 esis\nis to explore online tree construction algorithms and demonstrate th
 e\ntechnique and effectiveness of distilling reinforcement learning\npolic
 ies into a Bayesian tree structure.
DTSTAMP:20260318T022118Z
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