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A fully adaptive framework for continuous-state stochastic dynamic programming
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DOI：10.1016/j.cor.2025.107160.png)
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Abstract
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• Proposes a novel adaptive dynamic programming methodology to solve a high-dimensional, continuous-state, multistage, stochastic dynamic programming (SDP) problem. • Presents a methodology with a unique ability to automatically and adaptively identify the state space, sample size, and statistical model structure for future value function approximation for an SDP problem. • Demonstrates the efficiency and efficacy of the proposed methodology using a nine- dimensional inventory forecasting problem.
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