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multiRL  

Reinforcement Learning Tools for Multi-Armed Bandit
View on CRAN: Click here


Download and install multiRL package within the R console
Install from CRAN:
install.packages("multiRL")

Install from Github:
library("remotes")
install_github("cran/multiRL")

Install by package version:
library("remotes")
install_version("multiRL", "0.4.5")



Attach the package and use:
library("multiRL")
Maintained by
YuKi
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-01-26
Latest Update: 2026-01-26
Description:
A flexible general-purpose toolbox for implementing Rescorla-Wagner models in multi-armed bandit tasks. As the successor and functional extension of the 'binaryRL' package, 'multiRL' modularizes the Markov Decision Process (MDP) into six core components. This framework enables users to construct custom models via intuitive if-else syntax and define latent learning rules for agents. For parameter estimation, it provides both likelihood-based inference (MLE and MAP) and simulation-based inference (ABC and RNN), with full support for parallel processing across subjects. The workflow is highly standardized, featuring four main functions that strictly follow the four-step protocol (and ten rules) proposed by Wilson & Collins (2019) <doi:10.7554/eLife.49547>. Beyond the three built-in models (TD, RSTD, and Utility), users can easily derive new variants by declaring which variables are treated as free parameters.
How to cite:
YuKi (2026). multiRL: Reinforcement Learning Tools for Multi-Armed Bandit. R package version 0.4.5, https://cran.r-project.org/web/packages/multiRL. Accessed 03 Oct. 2026.
Previous versions and publish date:
(2026-07-09 06:33), 0.2.3 (2026-01-26 17:20), 0.3.7 (2026-03-31 13:00)
Other packages that cited multiRL R package
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Functions, R codes and Examples using the multiRL R package
Full multiRL package functions and examples
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Maintainer: Lu Mao (view profile)

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