Package: ReinforcementLearning 1.0.5

ReinforcementLearning: Model-Free Reinforcement Learning

Performs model-free reinforcement learning in R. This implementation enables the learning of an optimal policy based on sample sequences consisting of states, actions and rewards. In addition, it supplies multiple predefined reinforcement learning algorithms, such as experience replay. Methodological details can be found in Sutton and Barto (1998) <ISBN:0262039249>.

Authors:Nicolas Proellochs [aut, cre], Stefan Feuerriegel [aut]

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NEWS

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

Bug tracker:https://github.com/nproellochs/reinforcementlearning/issues

Datasets:
  • tictactoe - Game states of 100,000 randomly sampled Tic-Tac-Toe games.

On CRAN:

Conda-Forge:

experience-samplingreinforcement-learning

7.33 score 68 stars 1 packages 210 scripts 380 downloads 13 exports 30 dependencies

Last updated 5 years agofrom:b14091a532. Checks:1 OK, 7 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 08 2025
R-4.5-winNOTEFeb 08 2025
R-4.5-macNOTEFeb 08 2025
R-4.5-linuxNOTEFeb 08 2025
R-4.4-winNOTEFeb 08 2025
R-4.4-macNOTEFeb 08 2025
R-4.3-winNOTEFeb 08 2025
R-4.3-macNOTEFeb 08 2025

Exports:computePolicyepsilonGreedyActionSelectionexperienceReplaygridworldEnvironmentpolicyrandomActionSelectionReinforcementLearningreplayExperiencesampleExperiencesampleGridSequenceselectEpsilonGreedyActionselectRandomActionstate

Dependencies:clicolorspacedata.tablefansifarverggplot2gluegtablehashisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr

Reinforcement Learning in R

Rendered fromReinforcementLearning.Rmdusingknitr::rmarkdownon Feb 08 2025.

Last update: 2019-05-24
Started: 2017-03-29