Multi-objective dynamic programming with limited precision

L. Mandow (), J. L. Perez- de-la-Cruz () and N. Pozas ()
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L. Mandow: Universidad de Málaga, Andalucía Tech
J. L. Perez- de-la-Cruz: Universidad de Málaga, Andalucía Tech
N. Pozas: Universidad de Málaga, Andalucía Tech

Journal of Global Optimization, 2022, vol. 82, issue 3, No 8, 595-614

Abstract: Abstract This paper addresses the problem of approximating the set of all solutions for Multi-objective Markov Decision Processes. We show that in the vast majority of interesting cases, the number of solutions is exponential or even infinite. In order to overcome this difficulty we propose to approximate the set of all solutions by means of a limited precision approach based on White’s multi-objective value-iteration dynamic programming algorithm. We prove that the number of calculated solutions is tractable and show experimentally that the solutions obtained are a good approximation of the true Pareto front.

Keywords: Reinforcement learning; Multi-objective; Markov decision processes; Dynamic programming (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10898-021-01096-x

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