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multiobjectiveMDP: Solution Methods for Multi-Objective Markov Decision Processes

Compendium of the most representative algorithms in print—vector-valued dynamic programming, linear programming, policy iteration, the weighting factor approach—for solving multi-objective Markov decision processes, with or without reward discount, over a finite or infinite horizon. Mifrani, A. (2024) <doi:10.1007/s10479-024-06439-x>; Mifrani, A. & Noll, D. <doi:10.48550/arXiv.2502.13697>; Wakuta, K. (1995) <doi:10.1016/0304-4149(94)00064-Z>.

Version: 1.0.0
Imports: dplyr, linprog, lintools, nsga2R, pracma, prodlim, stats
Suggests: knitr, rmarkdown
Published: 2026-03-06
DOI: 10.32614/CRAN.package.multiobjectiveMDP
Author: Anas Mifrani ORCID iD [aut, cre, cph]
Maintainer: Anas Mifrani <anas.mifrani at math.univ-toulouse.fr>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
CRAN checks: multiobjectiveMDP results

Documentation:

Reference manual: multiobjectiveMDP.html , multiobjectiveMDP.pdf
Vignettes: Introduction to multiobjectiveMDP (source, R code)

Downloads:

Package source: multiobjectiveMDP_1.0.0.tar.gz
Windows binaries: r-devel: multiobjectiveMDP_1.0.0.zip, r-release: not available, r-oldrel: multiobjectiveMDP_1.0.0.zip
macOS binaries: r-release (arm64): multiobjectiveMDP_1.0.0.tgz, r-oldrel (arm64): multiobjectiveMDP_1.0.0.tgz, r-release (x86_64): multiobjectiveMDP_1.0.0.tgz, r-oldrel (x86_64): multiobjectiveMDP_1.0.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.