Package: RLoptimal 1.1.1

Kentaro Matsuura

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.

Authors:Kentaro Matsuura [aut, cre, cph], Koji Makiyama [aut, ctb]

RLoptimal_1.1.1.tar.gz
RLoptimal_1.1.1.tar.gz(r-4.5-noble)RLoptimal_1.1.1.tar.gz(r-4.4-noble)
RLoptimal_1.1.1.tgz(r-4.4-emscripten)RLoptimal_1.1.1.tgz(r-4.3-emscripten)
RLoptimal.pdf |RLoptimal.html✨
RLoptimal/json (API)
NEWS

# Install 'RLoptimal' in R:

install.packages('RLoptimal', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/matsuurakentaro/rloptimal/issues

3.50 score 21 scripts 366 downloads 8 exports 38 dependencies

Last updated 3 days agofrom:e7973134b7. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Nov 22 2024
R-4.5-linux	OK	Nov 22 2024

Exports:adjust_significance_level AllocationRule clean_python_settings learn_allocation_rule rl_config_set rl_dnn_config setup_python simulate_one_trial

Dependencies:cli colorspace DoseFinding fansi farver ggplot2 glue gtable here isoband jsonlite labeling lattice lifecycle magrittr MASS Matrix mgcv munsell mvtnorm nlme pillar pkgconfig png R6 rappdirs RColorBrewer Rcpp RcppTOML reticulate rlang rprojroot scales tibble utf8 vctrs viridisLite withr

Optimal Adaptive Allocation Using Deep Reinforcement Learning

Kentaro Matsuura

Rendered fromRLoptimal.Rmdusingknitr::rmarkdownon Nov 22 2024.

Last update: 2024-11-03
Started: 2024-10-04

Citation

Readme and manuals

Help Manual

Help page	Topics
Adjust Significance Level on a Simulation Basis	adjust_significance_level
Allocation Rule Class	AllocationRule
Clean the Python Virtual Environment	clean_python_settings
Build an Optimal Adaptive Allocation Rule using Reinforcement Learning	learn_allocation_rule
Configuration of Reinforcement Learning	rl_config_set
DNN Configuration for Reinforcement Learning	rl_dnn_config
Setting up a Python Virtual Environment	setup_python
Simulate One Trial Using an Obtained Optimal Adaptive Allocation Rule	simulate_one_trial