RLoptimal: Optimal Adaptive Allocation Using Deep Reinforcement Learning
An implementation to compute an optimal adaptive allocation rule using deep reinforcement learning in a dose-response study (Matsuura et al. (2022) <doi:10.1002/sim.9247>). The adaptive allocation rule can directly optimize a performance metric, such as power, accuracy of the estimated target dose, or mean absolute error over the estimated dose-response curve.
| Version: | 1.2.2 |
| Imports: | DoseFinding,glue,R6,reticulate, stats, utils,zip |
| Suggests: | knitr,rmarkdown,testthat (≥ 3.0.0) |
| Published: | 2025-10-02 |
| DOI: | 10.32614/CRAN.package.RLoptimal |
| Author: | Kentaro Matsuura [aut, cre, cph], Koji Makiyama [aut, ctb] |
| Maintainer: | Kentaro Matsuura <matsuurakentaro55 at gmail.com> |
| BugReports: | https://github.com/MatsuuraKentaro/RLoptimal/issues |
| License: | MIT + fileLICENSE |
| URL: | https://github.com/MatsuuraKentaro/RLoptimal |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | README,NEWS |
| CRAN checks: | RLoptimal results |
Documentation:
Downloads:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=RLoptimalto link to this page.