Nonlinear Algebra and Statistics (NLASTATS) Seminar by Ivan Gvozdanovic: Introduction to Reinforcement Learning and its Application in Economics




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Speaker: Ivan Gvozdanovic, Applied Mathematics, Illinois Institute of Technology

Title: Introduction to Reinforcement Learning and its Application in Economics.

Abstract: Reinforcement Learning has been a prominent area of research for the last 2 decades. It has seen major expansion in recent times with the development of Neural Networks and more powerful computing machines. One of the most desirable aspects of RL, can be found in the class of model-free algorithms which can learn optimal policies without the knowledge of the underlying data generation process. We split this talk into 2 parts. The first part constitutes the formulation of a discrete time Markov Decision Process as the basis of RL. We provide an overview of two groups of classical RL algorithms, value-based and policy-based. In the second part, we present our latest work in the development of algorithms designed to approximate solutions to recursive economic utility such as Epstein-Zin (EZ) utility preference. We compare and contrast their convergence properties on the optimal consumption portfolio problem with an EZ utility. This is joint work with Professor Matthew Dixon. 


Nonlinear Algebra and Statistics Seminar


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