Events-Reinforcement Learning in Continuous Time

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Reinforcement Learning in Continuous Time

Jun 2, 2023, 16:30-17:30

Abstract

In this talk I will report the recent research development on reinforcement learning in continuous time, including exploratory formulation, Gibbs sampler, Gaussian exploration, policy evaluation, policy gradient, and on-/off-policy q-learning theory.

About the speaker

Xunyu Zhou is the Liu Family Professor of Financial Engineering and the Director of the Nie Center for Intelligent Asset Management at Columbia University. He was the Nomura Professor of Mathematical Finance at University of Oxford before joining Columbia in 2016. His research covers stochastic control, dynamic portfolio selection, asset pricing, behavioral finance, and time inconsistency. Currently his research focuses on continuous-time reinforcement learning and applications to optimization broadly and to wealth management specifically. He is a recipient of the Wolfson Research Award from The Royal Society, the Outstanding Paper Prize from SIAM, the Alexander von Humboldt Research Fellowship, and the Croucher Senior Research Fellowship. He was an invited speaker at the 2010 International Congress of Mathematicians, a Humboldt Distinguished Lecturer at Humboldt University and an Archimedes Lecturer at Columbia. He is both an IEEE Fellow and a SIAM Fellow.
Xunyu Zhou received his PhD in Operations Research and Control Theory from Fudan University in 1989.