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'))

Peer review:

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

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

On CRAN:

experience-samplingreinforcement-learning

7.33 score 67 stars 1 packages 213 scripts 458 downloads 13 exports 30 dependencies

Last updated 5 years agofrom:b14091a532. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-winNOTENov 10 2024
R-4.5-linuxNOTENov 10 2024
R-4.4-winNOTENov 10 2024
R-4.4-macNOTENov 10 2024
R-4.3-winNOTENov 10 2024
R-4.3-macNOTENov 10 2024

Exports:computePolicyepsilonGreedyActionSelectionexperienceReplaygridworldEnvironmentpolicyrandomActionSelectionReinforcementLearningreplayExperiencesampleExperiencesampleGridSequenceselectEpsilonGreedyActionselectRandomActionstate

Dependencies:clicolorspacedata.tablefansifarverggplot2gluegtablehashisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr

Reinforcement Learning in R

Rendered fromReinforcementLearning.Rmdusingknitr::rmarkdownon Nov 10 2024.

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