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X-WR-CALNAME:Curiously effective ensemble and double-oracle reinforcement-l
earning methods
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UID:2022-01-10-roy-fox@cml.ics.uci.edu
DTSTAMP:20220110T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20220110T130000
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SUMMARY:[CML Seminar] Roy Fox: Curiously effective ensemble and double-orac
le reinforcement-learning methods
LOCATION:Online (live stream)
DESCRIPTION:Roy Fox\, Assistant Professor\, Department of Computer Science\
, University of California\, Irvine\n\nTitle: Curiously effective ensemble
and double-oracle reinforcement-learning methods\n\nAbstract: Ensemble me
thods for reinforcement learning represent model uncertainty and use it to
guide exploration and reduce value estimation bias. We present MeanQ\, a
very simple ensemble method with improved performance that reduces estimat
ion variance enough to operate without a stabilizing target network — cu
riously\, it is theoretically almost equivalent to a non-ensemble method i
t significantly outperforms. In adversarial environments\, double-oracle (
DO) methods grow a population of policies by iteratively adding best respo
nses. We present XDO\, a DO algorithm that exploits the game's sequential
structure to exponentially reduce the worst-case population size.\n\nhttps
://cml.ics.uci.edu/seminars/2022-01-10-roy-fox
X-ALT-DESC;FMTTYPE=text/html:Roy Fox\, Assistant Profess
or\, Department of Computer Science\, University of California\, Irvine
Title: Curiously effective ensemble and double-oracle reinforc
ement-learning methods
Abstract: Ensemble methods for reinfo
rcement learning represent model uncertainty and use it to guide explorati
on and reduce value estimation bias. We present MeanQ\, a very simple ense
mble method with improved performance that reduces estimation variance eno
ugh to operate without a stabilizing target network — curiously\, it is
theoretically almost equivalent to a non-ensemble method it significantly
outperforms. In adversarial environments\, double-oracle (DO) methods grow
a population of policies by iteratively adding best responses. We present
XDO\, a DO algorithm that exploits the game's sequential structure to exp
onentially reduce the worst-case population size.
https://cml.ics.uci.edu/semin
ars/2022-01-10-roy-fox
URL:https://cml.ics.uci.edu/seminars/2022-01-10-roy-fox
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