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: 1.0.1
Imports: DoseFinding, glue, R6, reticulate, stats, utils
Suggests: knitr, rmarkdown
Published: 2024-10-03
DOI: 10.32614/CRAN.package.RLoptimal
Author: Kentaro Matsuura ORCID iD [aut, cre, cph], Koji Makiyama [aut, ctb]
Maintainer: Kentaro Matsuura <matsuurakentaro55 at gmail.com>
BugReports: https://github.com/MatsuuraKentaro/RLoptimal/issues
License: MIT + file LICENSE
URL: https://github.com/MatsuuraKentaro/RLoptimal
NeedsCompilation: no
Language: en_US
Materials: README NEWS
CRAN checks: RLoptimal results

Documentation:

Reference manual: RLoptimal.pdf
Vignettes: Optimal Adaptive Allocation Using Deep Reinforcement Learning (source, R code)

Downloads:

Package source: RLoptimal_1.0.1.tar.gz
Windows binaries: r-devel: RLoptimal_1.0.1.zip, r-release: RLoptimal_1.0.1.zip, r-oldrel: RLoptimal_1.0.1.zip
macOS binaries: r-release (arm64): RLoptimal_1.0.1.tgz, r-oldrel (arm64): RLoptimal_1.0.1.tgz, r-release (x86_64): RLoptimal_1.0.1.tgz, r-oldrel (x86_64): RLoptimal_1.0.1.tgz

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