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SUMMARY:Pedro A. Santos (Instituto Superior Técnico and INESC-ID)
DTSTART:20210409T130000Z
DTEND:20210409T140000Z
DTSTAMP:20260423T003246Z
UID:MPML/40
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/40/">Tw
 o-time scale stochastic approximation for reinforcement learning with line
 ar function approximation</a>\nby Pedro A. Santos (Instituto Superior Téc
 nico and INESC-ID) as part of Mathematics\, Physics and Machine Learning (
 IST\, Lisbon)\n\n\nAbstract\nIn this presentation\, I will introduce some 
 traditional Reinforcement Learning problems and algorithms\, and analyze h
 ow some problems can be avoided and convergence results obtained using a t
 wo-time scale variation of the usual stochastic approximation approach.\n\
 nThis variation was inspired by the practical successes of Deep Q-Learning
  in attaining superhuman performance at some classical Atari games by Deep
 mind's research team in 2015. Machine Learning practical successes like th
 is often have no corresponding explaining theory. The work that will be pr
 esented intends to contribute to that goal.\n\nJoint work with Diogo Carva
 lho and Francisco Melo from INESC-ID.\n
LOCATION:https://researchseminars.org/talk/MPML/40/
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