EPyT-Control -- EPANET Python Toolkit - Control

EPyT-Control is a Python package building on top of EPyT-Flow for implementing and evaluating control algorithms & strategies in water distribution networks (WDNs).

Besides related control tasks such as state estimation and event diagnosis, a special focus of this Python package is Reinforcement Learning for data-driven control in WDNs and therefore it provides full compatibility with the Stable-Baselines3 package.

Unique Features

Unique features of EPyT-Control are the following:

• Support of hydraulic and (advanced) water quality simulation (i.e. EPANET and EPANET-MSX are supported)
• Compatibility with Gymnasium and integration of Stable-Baselines3
• Wide variety of pre-defined actions (e.g. pump state actions, pump speed actons, valve state actions, species injection actions, etc.)
• Implementation of classic control aglorithms such as PID and LQR controllers
• Signal processing methods such as state estimation (e.g. Kalman filters) and event diagnosis
• High- and low-level interface
• Object-orientated design that is easy to extend and customize

Installation

EPyT-Control supports Python 3.9 - 3.13

PyPI

pip install epyt-control

Git

Download or clone the repository:

git clone https://github.com/WaterFutures/EPyT-Control.git
cd EPyT-Control

Install all requirements as listed in REQUIREMENTS.txt:

pip install -r REQUIREMENTS.txt

Install the toolbox:

pip install .

Quick Example

Interface of Environments

Basic example demonstrating the environments' interface:

# Define/Specify MyEnv
# ....

# Load hypothetical environment "MyEnv"
with MyEnv() as env:
    # Show the observation space
    print(f"Observation space: {env.observation_space}")

    # Run 1000 iterations -- assuming that autorest=True
    obs, info = env.reset()
    for _ in range(1000):
        # Sample and apply a random action from the action space.
        # TODO: Replace with some smart RL/control method
        action = env.action_space.sample()
        obs, reward, terminated, _, _ = env.step(action)

        # Show action and observed reward
        print(action, reward)

Applying Reinforcement Learning to a given Environment

Simple example of using Stable-Baselines3 for learning a policy to control the chlorine injection in a given environment called SimpleChlorineInjectionEnv:

from stable_baselines3 import PPO
from gymnasium.wrappers import NormalizeObservation

# Define/Specify SimpleChlorineInjectionEnv
# ....

# Load chlorine injection environment
with SimpleChlorineInjectionEnv() as env:
    # Wrap environment
    env = NormalizeObservation(env)

    # Apply a simple policy learner
    # You might want to add more wrappers (e.g. normalizing inputs, rewards, etc.) and logging here
    # Also, inceasing the number of time steps might help as well
    model = PPO("MlpPolicy", env, verbose=1)
    model.learn(total_timesteps=1000)
    model.save("my_model_clinject.zip")  # Save policy

Documentation

Documentation is available on readthedocs: https://epyt-control.readthedocs.io/en/stable/

License

MIT license -- see LICENSE

How to Cite?

If you use this software, please cite it as follows:

@misc{github:epytcontrol,
        author = {André Artelt},
        title = {{EPyT-Control -- EPANET Python Toolkit - Control}},
        year = {2025},
        publisher = {GitHub},
        journal = {GitHub repository},
        howpublished = {\url{https://github.com/WaterFutures/EPyT-Control}}
    }

How to get Support?

If you come across any bug or need assistance please feel free to open a new issue if non of the existing issues answers your questions.

How to Contribute?

Contributions (e.g. creating issues, pull-requests, etc.) are welcome -- please make sure to read the code of conduct and follow the developers' guidelines.