Unbounded Dynamic Programming via the Q-Transform

Qingyin Ma, John Stachurski and Alexis Akira Toda

Papers from arXiv.org

Abstract: We propose a new approach to solving dynamic decision problems with unbounded rewards based on the transformations used in Q-learning. In our case, the objective of the transform is to convert an unbounded dynamic program into a bounded one. The approach is general enough to handle problems for which existing methods struggle, and yet simple relative to other techniques and accessible for applied work. We show by example that many common decision problems satisfy our conditions.

Date: 2020-11, Revised 2021-03
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Citations: View citations in EconPapers (2)

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Journal Article: Unbounded dynamic programming via the Q-transform (2022)
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