Bellman Error Minimization Via Linear Programming Normalization

Abstract:This paper proposes a new functional approximation approach to reduce Bellman error in high-dimensional dynamic programming and Reinforcement Learning problems. Using a classic dynamic programming problem (network capacity control in revenue management) as the motivational example, the paper illustrates that deep neural networks and linear programming approximation algorithms can be combined to derive approximate solutions to dynamic programming problems. Simulation results show the proposed approximation algorithms achieves competitive performance when compared with benchmark.

Machine Learning (cs.LG)

arXiv:2610.02730 [cs.LG]

(or arXiv:2610.02730v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.02730 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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