RLoptimal | Overview | Installation | Example | Build dose-response models for MCPMod | Learn an allocation rule | How to use the allocation rule | Simulation-based adjustment of the significance level | How to evaluate the allocation rule | Tips | Dose-response models for reinforcement learning | Binary outcome | How to use Allocation Rule Class | Save the allocation rule | Inputs of learn_allocation_rule function | Obtain returns during reinforcement learning | Resume learning | Use checkpoint
Package: RLoptimal 1.2.2
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:
RLoptimal_1.2.2.tar.gz
RLoptimal_1.2.2.zip(r-4.7-any)RLoptimal_1.2.2.zip(r-4.6-any)RLoptimal_1.2.2.zip(r-4.5-any)
RLoptimal_1.2.2.tgz(r-4.6-any)RLoptimal_1.2.2.tgz(r-4.5-any)
RLoptimal_1.2.2.tar.gz(r-4.7-any)RLoptimal_1.2.2.tar.gz(r-4.6-any)
RLoptimal_1.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
RLoptimal/json (API)
| # Install 'RLoptimal' in R: |
| install.packages('RLoptimal', repos = c('https://matsuurakentaro.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/matsuurakentaro/rloptimal/issues
Last updated from:d594feee85. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 165 | ||
| source / vignettes | OK | 172 | ||
| linux-release | OK | 130 | ||
| macos-release | OK | 155 | ||
| macos-oldrel | OK | 176 | ||
| windows-devel | OK | 75 | ||
| windows-release | OK | 74 | ||
| windows-oldrel | OK | 80 | ||
| wasm-release | OK | 135 |
Exports:adjust_significance_levelAllocationRuleclean_python_settingslearn_allocation_rulerl_config_setrl_dnn_configsetup_pythonsimulate_one_trial
Dependencies:clicpp11DoseFindingfarverggplot2gluegtablehereisobandjsonlitelabelinglatticelifecycleMatrixmvtnormpngR6rappdirsRColorBrewerRcppRcppTOMLreticulaterlangrprojrootS7scalesvctrsviridisLitewithrzip
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 |