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binaryRL: Reinforcement Learning Tools for Two-Alternative Forced Choice
Tasks
Tools for building reinforcement learning (RL) models
specifically tailored for Two-Alternative Forced Choice (TAFC) tasks,
commonly employed in psychological research. These models build upon
the foundational principles of model-free reinforcement learning detailed in
Sutton and Barto (1998) <ISBN:0262039249>. The package allows
for the intuitive definition of RL models using simple if-else
statements. Our approach to constructing and evaluating these
computational models is informed by the guidelines proposed in
Wilson & Collins (2019) <doi:10.7554/eLife.49547>. Example
datasets included with the package are sourced from the work of
Mason et al. (2024) <doi:10.3758/s13423-023-02415-x>.
| Version: |
0.8.0 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
future, doFuture, foreach, doRNG, progressr |
| Suggests: |
stats, GenSA, GA, DEoptim, mlrMBO, mlr, ParamHelpers, smoof, lhs, pso, cmaes |
| Published: |
2025-05-13 |
| DOI: |
10.32614/CRAN.package.binaryRL |
| Author: |
YuKi  [aut, cre] |
| Maintainer: |
YuKi <hmz1969a at gmail.com> |
| BugReports: |
https://github.com/yuki-961004/binaryRL/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/yuki-961004/binaryRL |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
binaryRL results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=binaryRL
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.