A Reinforcement Learning Approach to Solving Incomplete Market Models with Aggregate Uncertainty

20 Pages Posted: 9 May 2011

Date Written: May 5, 2011

Abstract

We develop a method of solving heterogeneous agent models in which individual decisions depend on the entire cross-sectional distribution of individual state variables, such as incomplete market models with liquidity constraints. Our method is based on the principle of reinforcement learning, and does not require parametric assumptions on either the agents' information set, or on the functional form of the aggregate dynamics.

Keywords: heterogeneous agents, macroeconomics, dynamic programming, reinforcement learning

JEL Classification: C63, C68, E20

Suggested Citation

Jirnyi, Andrei and Lepetyuk, Vadym, A Reinforcement Learning Approach to Solving Incomplete Market Models with Aggregate Uncertainty (May 5, 2011). Available at SSRN: https://ssrn.com/abstract=1832745 or http://dx.doi.org/10.2139/ssrn.1832745

Andrei Jirnyi (Contact Author)

Northwestern University - Kellogg School of Management ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Vadym Lepetyuk

Universidad de Alicante ( email )

Campus de San Vicente
Carretera San Vicente del Raspeig
San Vicente del Raspeig, Alicante 03690
Spain

Government of Canada - Bank of Canada ( email )

234 Wellington Street
Ontario, Ottawa K1A 0G9
Canada

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