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]

ReinforcementLearning_1.0.5.tar.gz
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manual.pdf |manual.html
card.svg |card.png
ReinforcementLearning/json (API)
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:

experience-samplingreinforcement-learning

7.34 score 69 stars 1 packages 213 scripts 257 downloads 13 exports 19 dependencies

Last updated from:b14091a532. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE125
source / vignettesOK172
linux-release-x86_64NOTE121
macos-release-arm64NOTE194
macos-oldrel-arm64NOTE214
windows-develNOTE85
windows-releaseNOTE80
windows-oldrelNOTE83
wasm-releaseOK104

Exports:computePolicyepsilonGreedyActionSelectionexperienceReplaygridworldEnvironmentpolicyrandomActionSelectionReinforcementLearningreplayExperiencesampleExperiencesampleGridSequenceselectEpsilonGreedyActionselectRandomActionstate

Dependencies:clicpp11data.tablefarverggplot2gluegtablehashisobandlabelinglifecycleR6RColorBrewerrlangS7scalesvctrsviridisLitewithr

Reinforcement Learning in R

Rendered fromReinforcementLearning.Rmdusingknitr::rmarkdownon May 31 2026.

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