PhysiGym is a tool for applying reinforcement learning to PhysiCell
Learning Dynamic Treatment Regime (DTR) via meta-learners
Companion code for the following paper: https://doi.org/10.1093/biostatistics/kxad035
This repository contains code to estimate sample size needed to compare dynamic treatment regimens using longitudinal count outcomes from a Sequential Multiple Assignment Randomized Trial (SMART).
Experiments in dynamic treatment regimes using reinforcement learning.
Code and Datasets for the paper "Deconfounding actor-critic network with policy adaptation for dynamic treatment regimes", published on KDD 2022.
We have presented CIL method to learn the optimal dynamic treatment regime by exploiting information from both trajectories (positive and negative).
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