Package: RLoptimal 1.0.1.9000
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_1.0.1.9000.tar.gz
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RLoptimal.pdf |RLoptimal.html✨
RLoptimal/json (API)
NEWS
| # 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 3 days agofrom:1eb3b2b8ba. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 26 2024 |
| R-4.5-win | OK | Oct 26 2024 |
| R-4.5-linux | OK | Oct 26 2024 |
| R-4.4-win | OK | Oct 26 2024 |
| R-4.4-mac | OK | Oct 26 2024 |
| R-4.3-win | OK | Oct 26 2024 |
| R-4.3-mac | OK | Oct 26 2024 |
Exports:adjust_significance_levelAllocationRuleclean_python_settingslearn_allocation_rulerl_config_setrl_dnn_configsetup_pythonsimulate_one_trial
Dependencies:clicolorspaceDoseFindingfansifarverggplot2gluegtablehereisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellmvtnormnlmepillarpkgconfigpngR6rappdirsRColorBrewerRcppRcppTOMLreticulaterlangrprojrootscalestibbleutf8vctrsviridisLitewithr
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 |
