Data and code from: Deep reinforcement learning for pressure optimization in water distribution networks with multiple pumping stations: Case study

This dataset provides the complete code, model files, and tabular data used to train and evaluate a deep reinforcement learning (DRL) agent for pressure optimization in large-scale water distribution networks with multiple pumping stations. It includes ten Python scripts (for environment definition, training, testing, and evaluation), calibrated EPANET hydraulic model files, and eight Excel workbooks containing control parameters, diurnal demand data, synthetic and observed evaluation sets, and energy-performance summaries. The dataset enables full reproduction of the case-study results and supports reuse for developing alternative DRL algorithms or benchmarking water-network optimization methods. The repository is self-contained and can be executed using Python 3.10 with the package versions specified herein.
, , # Data and code from: Deep reinforcement learning for pressure optimization in water distribution networks with multiple pumping stations: Case study

## Overview

This README file provides detailed documentation for the dataset *Deep Reinforcement Learning for Pressure Optimization in Water Distribution Networks with Multiple Pumping Stations: Case Study*, ASCE Journal of Water Resources Planning and Management. It describes the folder structure, variable definitions, software environment, and step-by-step instructions required to reproduce the analyses and results. The abstract for this dataset is provided separately on the Dryad record page.

## 1. Directory Structure and File Descriptions

```
DataSetR02.zip/
│
├── Python Scripts
│ ├── env_001.py → Custom Gym environment for multi-pump network.
│ ├── evaluate_001.py → Evaluates trained SAC agent on validation sets.
│ ├── Evaluation_conventional_001.py → Baseline evaluation using fixed set...,