Package: MDPtoolbox 4.0.4

MDPtoolbox: Markov Decision Processes Toolbox

The Markov Decision Processes (MDP) toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes: finite horizon, value iteration, policy iteration, linear programming algorithms with some variants and also proposes some functions related to Reinforcement Learning.

Authors:Iadine Chades [aut], Guillaume Chapron [aut, cre], Marie-Josee Cros [aut], Frederick Garcia [aut], Regis Sabbadin [aut]

MDPtoolbox_4.0.4.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MDPtoolbox/json (API)

# Install 'MDPtoolbox' in R:
install.packages('MDPtoolbox', repos = c('https://gchapron.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.

2.57 score 3 stars 123 scripts 436 downloads 21 exports 4 dependencies

Last updated from:d0561522d4. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK143
source / vignettesOK215
linux-releaseOK110
macos-releaseOK158
macos-oldrelOK211
windows-develOK77
windows-releaseOK70
windows-oldrelOK74
wasm-releaseOK98

Exports:mdp_bellman_operatormdp_checkmdp_check_square_stochasticmdp_computePpolicyPRpolicymdp_computePRmdp_eval_policy_iterativemdp_eval_policy_matrixmdp_eval_policy_optimalitymdp_eval_policy_TD_0mdp_example_forestmdp_example_randmdp_finite_horizonmdp_LPmdp_policy_iterationmdp_policy_iteration_modifiedmdp_Q_learningmdp_relative_value_iterationmdp_spanmdp_value_iterationmdp_value_iteration_bound_itermdp_value_iterationGS

Dependencies:latticelinproglpSolveMatrix

Readme and manuals

Help Manual

Help pageTopics
Markov Decision Processes ToolboxMDPtoolbox-package MDPtoolbox
Applies the Bellman operatormdp_bellman_operator
Checks the validity of a MDPmdp_check
Checks if a matrix is square and stochasticmdp_check_square_stochastic
Computes the transition matrix and the reward matrix for a fixed policymdp_computePpolicyPRpolicy
Computes a reward matrix for any form of transition and reward functionsmdp_computePR
Evaluates a policy using an iterative methodmdp_eval_policy_iterative
Evaluates a policy using matrix inversion and productmdp_eval_policy_matrix
Computes sets of 'near optimal' actions for each statemdp_eval_policy_optimality
Evaluates a policy using the TD(0) algorithmmdp_eval_policy_TD_0
Generates a MDP for a simple forest management problemmdp_example_forest
Generates a random MDP problemmdp_example_rand
Solves finite-horizon MDP using backwards induction algorithmmdp_finite_horizon
Solves discounted MDP using linear programming algorithmmdp_LP
Solves discounted MDP using policy iteration algorithmmdp_policy_iteration
Solves discounted MDP using modified policy iteration algorithmmdp_policy_iteration_modified
Solves discounted MDP using the Q-learning algorithm (Reinforcement Learning)mdp_Q_learning
Solves MDP with average reward using relative value iteration algorithmmdp_relative_value_iteration
Evaluates the span of a vectormdp_span
Solves discounted MDP using value iteration algorithmmdp_value_iteration
Computes a bound for the number of iterations for the value iteration algorithmmdp_value_iteration_bound_iter
Solves discounted MDP using Gauss-Seidel's value iteration algorithmmdp_value_iterationGS