Package: multiobjectiveMDP 1.0.0

Anas Mifrani

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>.

Authors:Anas Mifrani [aut, cre, cph]

multiobjectiveMDP_1.0.0.tar.gz
multiobjectiveMDP_1.0.0.tar.gz(r-4.7-any)multiobjectiveMDP_1.0.0.tar.gz(r-4.6-any)
multiobjectiveMDP_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
multiobjectiveMDP/json (API)
NEWS

# Install 'multiobjectiveMDP' in R:
install.packages('multiobjectiveMDP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 161 downloads 23 exports 51 dependencies

Last updated from:09555c16fb. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK139
source / vignettesOK201
linux-release-x86_64OK151
wasm-releaseOK120

Exports:are_valid_finite_horizon_rewardsare_valid_finite_horizon_transition_probabilitiesare_valid_infinite_horizon_rewardsare_valid_infinite_horizon_transition_probabilitiescompromise_solutiondiscounted_bellman_operatorefficient_subset_sort_pruneevaluate_discounted_MMDP_pure_policyevaluate_finite_horizon_MMDP_markov_policygenerate_rand_MMDPis_valid_finite_horizon_policyis_valid_infinite_horizon_policysolve_discounted_MDP_policy_iterationsolve_discounted_MMDP_linear_programmingsolve_discounted_MMDP_policy_iterationsolve_discounted_MMDP_weighting_factorsolve_finite_horizon_MDP_backward_inductionsolve_finite_horizon_MMDP_backward_inductionsolve_finite_horizon_MMDP_linear_programmingsolve_finite_horizon_MMDP_weighting_factorsolve_MOLPsum_setvalue_function_domination_sets

Dependencies:clicodetoolscpp11data.tablediagramdigestdplyrfarverfuturefuture.applygenericsggplot2globalsgluegtableisobandKernSmoothlabelinglatticelavalifecyclelinproglintoolslistenvlpSolvemagrittrMatrixmconsga2RnumDerivparallellypillarpkgconfigpracmaprodlimprogressrR6RColorBrewerRcpprlangS7scalesshapeSQUAREMsurvivaltibbletidyselectutf8vctrsviridisLitewithr

Introduction to multiobjectiveMDP

Rendered fromIntroduction-to-multiobjectiveMDP.Rmdusingknitr::rmarkdownon May 05 2026.

Last update: 2026-03-06
Started: 2026-03-06

Readme and manuals

Help Manual

Help pageTopics
Determine whether a numeric list represents a valid reward structure for finite-horizon problemsare_valid_finite_horizon_rewards
Determine whether a numeric list represents a valid transition probability structure for finite-horizon problemsare_valid_finite_horizon_transition_probabilities
Determine whether a numeric list represents a valid reward structure for infinite-horizon problemsare_valid_infinite_horizon_rewards
Determine whether a numeric list represents a valid transition probability structure for infinite-horizon problemsare_valid_infinite_horizon_transition_probabilities
Calculate the compromise solution among a set of objective vectorscompromise_solution
Apply a stationary Bellman-type operator to a vector-valued value functiondiscounted_bellman_operator
Find the Pareto efficient subset of a set of vectorsefficient_subset_sort_prune
Evaluate a stationary policy in a discounted infinite-horizon multi-objective Markov decision processevaluate_discounted_MMDP_pure_policy
Evaluate a Markov deterministic policy for a finite-horizon multi-objective Markov decision processevaluate_finite_horizon_MMDP_markov_policy
Generate a random instance of a multi-objective Markov decision processgenerate_rand_MMDP
Determine whether an integer matrix represents a policy for a given class of finite-horizon problemsis_valid_finite_horizon_policy
Determine whether an integer vector represents a stationary policy for a given class of infinite-horizon problemsis_valid_infinite_horizon_policy
Optimize a discounted infinite-horizon Markov decision process through policy iterationsolve_discounted_MDP_policy_iteration
Optimize a discounted infinite-horizon multi-objective Markov decision process through linear programmingsolve_discounted_MMDP_linear_programming
Optimize a discounted infinite-horizon multi-objective Markov decision process through policy iterationsolve_discounted_MMDP_policy_iteration
Optimize a discounted infinite-horizon multi-objective Markov decision process through the weighting factor approachsolve_discounted_MMDP_weighting_factor
Solve a standard finite-horizon Markov decision process through dynamic programmingsolve_finite_horizon_MDP_backward_induction
Optimize a finite-horizon multi-objective Markov decision process through vector-valued dynamic programmingsolve_finite_horizon_MMDP_backward_induction
Optimize a finite-horizon multi-objective Markov decision process through linear programmingsolve_finite_horizon_MMDP_linear_programming
Optimize a finite-horizon multi-objective Markov decision process through the weighting factor approachsolve_finite_horizon_MMDP_weighting_factor
Solve a multi-objective linear programming problem by a simplex-type methodsolve_MOLP
Calculate the sum set (Minkowski sum) of two or more sets of vectorssum_set
Compare two or more vector-valued value functionsvalue_function_domination_sets