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Risk-Averse Dynamic Programming for Markov Decision Processes

Andrzej Ruszczynski (rusz***at***business.rutgers.edu)

Abstract: We introduce the concept of a Markov risk measure and we use it to formulate risk-averse control problems for two Markov decision models: a finite horizon model and a discounted infinite horizon model. For both models we derive risk-averse dynamic programming equations and a value iteration method. For the infinite horizon problem we also develop a risk-averse policy iteration method and we prove its convergence. Finally, we propose a version of the Newton method to solve a nonsmooth equation arising in the policy iteration method and we prove its global convergence.

Keywords: Dynamic Risk Measures, Markov Risk Measures, Value Iteration, Policy Iteration, Nonsmooth Newton's Method

Category 1: Stochastic Programming

Category 2: Other Topics (Dynamic Programming )

Citation: Presented at the 20th International Symposium on Mathematical Programming, Chicago, August 23-28, 2009; appeared in Mathematical Programming, Series B, 2010; On-Line First, DOI: 10.1007/s10107-010-0393-3

Download: [PDF]

Entry Submitted: 12/19/2009
Entry Accepted: 12/19/2009
Entry Last Modified: 08/01/2010

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