BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//project/author//NONSGML v1.0//EN
CALSCALE:GREGORIAN
BEGIN:VEVENT
DTEND:20221015T120000Z
UID:c1e6c19182fb1c4b2446f9b3c2404842-341
DTSTAMP:19700101T120014Z
DESCRIPTION:Reinforcement Learning via Stochastic Approximation
URL;VALUE=URI:https://www.csa.iisc.ac.in/newweb/event/341/reinforcement-learning-via-stochastic-approximation/
SUMMARY:Reinforcement Learning (RL) is one of the most popular branches of AI/ML.  Moreover, many though not all of the currently popular RL algorithms have a solid mathematical justification.  At present, a wide variety of proof methods are used to study RL algorithms.  Therefore, there is scope for some unification of these diverse approaches.  In this talk, I will show how a large fraction of current RL algorithms can be viewed as implementations of the Stochastic Approximation (SA) algorithm, in various forms.  I will also show that, by using martingale methods, the convergence of these SA variants can be established more simply and with fewer assumptions than is possible using the ODE approach, which is another popular method of analyzing SA algorithms.  Some problems for future research will also be indicated.
DTSTART:20221015T120000Z
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