Learning algorithms for finite horizon constrained Markov decision processes

A. Mittal; N. Hemachandra

doi:10.3934/jimo.2007.3.429

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2007, Volume 3, Issue 3: 429-444. Doi: 10.3934/jimo.2007.3.429

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Learning algorithms for finite horizon constrained Markov decision processes

A. Mittal^1, and
N. Hemachandra^1,

1.
IE and OR Interdisciplinary Programme, IIT Bombay, Mumbai, 400 076

Received: April 2006

Revised: November 2006

Published: July 2007

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Abstract

We propose a heuristic and two stochastic approximation based learning algorithms for finite horizon, finite state-action constrained Markov decision models. We include models and numerical examples arising from risk management in fund allocation, retailer-depot product availability in a supply chain and admission control in a simple queue, that have to satisfy performance based constraints.

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Mathematics Subject Classification: Primary: 90B50; Secondary: 11K45, 90C40.

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