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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.
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Package details |
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| Author | Kentaro Matsuura [aut, cre, cph] (<https://orcid.org/0000-0001-5262-055X>), Koji Makiyama [aut, ctb] |
| Maintainer | Kentaro Matsuura <matsuurakentaro55@gmail.com> |
| License | MIT + file LICENSE |
| Version | 1.2.1 |
| URL | https://github.com/MatsuuraKentaro/RLoptimal |
| Package repository | View on CRAN |
| Installation |
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