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 # MGRL
 Multigroup Reinforcement learning for Electrolyte Repletion
+
+## Overview
+
+This repository presents a novel approach in healthcare data analytics, focusing on off-policy reinforcement learning methods for treatment policies. Our work is particularly tailored to the complex and diverse nature of patient populations, where individuals often present different chronic conditions requiring personalized treatment strategies.
+
+## Introduction
+
+Traditional reinforcement learning models in healthcare often overlook the heterogeneity within patient populations. To address this, our research implements a multi-group Gaussian process regression model within a fitted Q-iteration framework. This approach enables us to:
+
+- Model diverse patient subgroups accurately.
+- Tailor optimal treatment policies to each subgroup.
+- Estimate these functions across the entire patient population.
+
+## Application
+
+We apply our Multi-Group Reinforcement Learning (MGRL) framework to the critical problem of formulating optimal treatment policies for electrolyte repletion in patients with pre-existing medical conditions.
+
+## Key Features
+
+- **Multi-Group Gaussian Process (MGGP) Regression Models**: Allow for precise modeling of different patient subgroups.
+- **Fitted Q-Iteration Framework**: Ensures robust policy development and adaptation for each subgroup.
+- **Whole Population Estimation**: Provides a comprehensive view of the patient population, enhancing the treatment policy's overall effectiveness.
+