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Solving Large-Scale Allocation Problems with Partially Observable Outcomes

We introduce a new technique for solving large-scale allocation problems with partially observable states and constrained action and observation resources. Our decomposition uses a master linear program (LP) to determine allocations among a set of control policies, and uses partially observable Markov decision processes (POMDPs) to determine improving policies using dual prices from the master LP.