6533b857fe1ef96bd12b4cc0

RESEARCH PRODUCT

Mapping discounted and undiscounted Markov Decision Problems onto Hopfield neural networks

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description

This paper presents a framework for mapping the value-iteration and related successive approximation methods for Markov Decision Problems onto Hopfield neural networks, for both discounted and undiscounted versions of the finite state and action spaces. We analyse the asymptotic behaviour of the control sets and we give some estimates on the convergence rate for the value-iteration scheme. We relate the convergence properties on an energy function which represents the key point in mapping Markov Decision Problems onto Hopfield networks. Finally, an application from queueing systems in communication networks is taken into consideration and the results of computer simulation of Hopfield network running for the equivalent Markov Decision Problem are presented, together with some comments on possible developments.