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license: GPL-3.0-only
title: 'multiRL: Reinforcement Learning Tools for Multi-Armed Bandit'
version: 0.4.5
doi: 10.32614/CRAN.package.multiRL
abstract: 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) <https://doi.org/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.
authors:
- name: YuKi
  email: hmz1969a@gmail.com
  orcid: https://orcid.org/0009-0000-1378-1318
- name: Xinyu
  email: xinyu000328@gmail.com
  orcid: https://orcid.org/0009-0004-4974-9191
repository: https://yuki-961004.r-universe.dev
repository-code: https://github.com/yuki-961004/multiRL
commit: 326ab9225f276af02ccce03046713c8f8be8e09c
url: https://yuki-961004.github.io/multiRL/
date-released: '2026-05-30'
contact:
- name: YuKi
  email: hmz1969a@gmail.com
  orcid: https://orcid.org/0009-0000-1378-1318