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Abstract:
The curse of dimensionality gives rise to prohibitive
computational requirements that render infeasible the exact
solution of large-scale stochastic control problems. We study an
efficient method based on linear programming for approximating
solutions to such problems. The approach ``fits'' a linear
combination of pre-selected basis functions to the dynamic
programming cost-to-go function. We develop bounds on the
approximation error and present experimental results in the
domain of queueing network control, providing empirical support
for the methodology.
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