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UID:www.tcs.tifr.res.in/event/1373
DTSTAMP:20231226T095541Z
SUMMARY:Optimized Decision Making via Active Learning of Stochastic Hamilt
onians
DESCRIPTION:Speaker: Prof. Chandrajit Bajaj (University of Texas at Austin)
\n\nAbstract: \nA Hamiltonian represents the energy of a dynamical system
in phase space with coordinates of position and momentum. The Hamilton’s
equations of motion are obtainable as coupled symplectic differential equ
ations. In this talk I shall show how optimized decision making (action
sequences) can be obtained via a reinforcement learning problem wherein th
e agent interacts with the unknown environment to simultaneously learn a H
amiltonian surrogate and the optimal action sequences using Hamilton dynam
ics\, by invoking the Pontryagin Maximum Principle. We use optimal control
theory to define an optimal control gradient flow\, which guides the rein
forcement learning process of the agent to progressively optimize the Hami
ltonian while simultaneously converging to the optimal action sequence. Ex
tensions to stochastic Hamiltonians leading to stochastic action sequences
and the free-energy principle shall also be discussed.\nThis is joint wor
k with my students Taemin Heo\, Minh Nguyen.\n
URL:https://www.tcs.tifr.res.in/web/events/1373
DTSTART;TZID=Asia/Kolkata:20240123T160000
DTEND;TZID=Asia/Kolkata:20240123T170000
LOCATION:A-201 Seminar Room
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