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Learning algorithms for finite horizon constrained Markov decision processes

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


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