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ReinforcementLearning
View on CRAN: Click
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Download and install ReinforcementLearning package within the R console
Install from CRAN:
install.packages("ReinforcementLearning")
Install from Github:
library("remotes")
install_github("cran/ReinforcementLearning")
Install by package version:
library("remotes")
install_version("ReinforcementLearning", "1.0.5")
Attach the package and use:
library("ReinforcementLearning")
Maintained by
Nicolas Proellochs
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-04-18
Latest Update: 2020-03-02
Description:
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) .
How to cite:
Nicolas Proellochs (2017). ReinforcementLearning: Model-Free Reinforcement Learning. R package version 1.0.5, https://cran.r-project.org/web/packages/ReinforcementLearning. Accessed 03 Dec. 2024.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for ReinforcementLearning
Functions, R codes and Examples using
the ReinforcementLearning R package
Some associated functions: ReinforcementLearning . computePolicy . epsilonGreedyActionSelection . experienceReplay . gridworldEnvironment . lookupActionSelection . lookupLearningRule . policy . randomActionSelection . replayExperience . sampleExperience . sampleGridSequence . selectEpsilonGreedyAction . selectRandomAction . state . tictactoe .
Some associated R codes: ReinforcementLearning.R . actionSelection.R . data.R . experienceReplay.R . gridworld.R . learningRule.R . policy.R . sampleExperience.R . state.R . Full ReinforcementLearning package functions and examples
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