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• W2396906324 abstract "We consider the problem of budget (or other resource) allocation in sequential decision problems involving a large number of concurrently running sub-processes, whose only interaction is through their consumption of budget. Our first contribution is the introduction of budgeted MDPs (BMDPs), an MDP model in which policies/values are a function of available budget, (c.f. constrained MDPs which are solved given a fixed budget). BMDPs allow one to explicitly trade off allocated budget and expected value. We show that optimal value functions are concave, non-decreasing in budget, and piecewise-linear in the finite horizon case, and can be computed by dynamic programming (and support ready approximation). Our second contribution is a method that exploits BMDP solutions to allocate budget to a large number of independent BMDPs, coupled only by their common budget pool. The problem can be cast as a multiple-choice knapsack problem, which admits an efficient, optimal greedy algorithm. Empirical results in an online advertising domain confirm the efficacy of our methods." @default.
• W2396906324 created "2016-06-24" @default.
• W2396906324 creator A5036934218 @default.
• W2396906324 creator A5086690493 @default.
• W2396906324 date "2016-06-25" @default.
• W2396906324 modified "2023-09-23" @default.
• W2396906324 title "Budget allocation using weakly coupled, constrained Markov decision processes" @default.
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• W2396906324 hasPublicationYear "2016" @default.
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