Team-Wang-Lab/T-RL: Tree-based Reinforcement Learning for estimating optimal DTR.
We propose a tree-based reinforcement learning (T-RL) method to directly estimate optimal DTRs in a multi-stage multi-treatment setting. At each stage, T-RL builds an unsupervised decision tree that directly handles the problem of optimization with multiple treatment comparisons, through a purity measure constructed with augmented inverse probability weighted estimators.
Getting started
Browse package contents
Package details |
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| Author | Tebin Tao, Nina Zhou, Lu Wang |
| Maintainer | Lu Wang <luwang@umich.edu>, Nina Zhou <zhounina@umich.edu> |
| License | GPL (>= 2) |
| Version | 0.1.0 |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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