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: |
1.2.0 |
| Imports: |
DoseFinding, glue, R6, reticulate, stats, utils |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2024-12-21 |
| 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 + file LICENSE |
| URL: |
https://github.com/MatsuuraKentaro/RLoptimal |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README NEWS |
| CRAN checks: |
RLoptimal results |
Documentation:
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