This talk aims to introduce the basics of reinforcement learning (RL) and review its financial applications. In an RL framework, an intelligent agent learns to make and improve her decisions by interacting with the unknown environment, and by observing her state trajectories and a sequence of reward signals. RL provides a natural setting for decision-making problems where there are fewer assumptions needed on the underlying models. This talk starts with the introduction on Markov decision processes (MDP) which is the setting for many of the commonly used algorithms. Several popular RL algorithms will then be covered with details. Finally, we discuss the applications of these RL approaches in a variety of decision-making problems in finance including optimal execution, portfolio optimization, market making, and robo-advising.
This talk is based on a survey paper with Ben Hambly and Huining Yang.
About the Speaker:
Renyuan Xu is currently a WiSE Gabilan Assistant Professor in the Epstein Department of Industrial and Systems Engineering at the University of Southern California. Before joining USC, she spent two years as a Hooke Research Fellow in the Mathematical Institute at the University of Oxford mentored by Professor Rama Cont. She completed her Ph.D. in IEOR Department at UC Berkeley under the supervision of Professor Xin Guo in 2019. Her research interests lie broadly in the span of mathematical finance, stochastic analysis, game theory, and machine learning.
Zoom link: https://us02web.zoom.us/j/6817169181?pwd=bG5SWVE1Y0NWVzd6b3JjTEVEU1EyUT09
ID: 681 716 9181
Your participation is warmly welcomed!