chemengRL 🎓

Educational notebooks of the reinforcement learning algorithms tabular Q-learning and DQN for chemical engineering applications.

Getting Started

Clone the Repository

To get started, clone the repository from GitHub:

git clone https://github.com/MaximilianB2/chemengRL.git
cd chemengRL

Set Up the Environment

You can use either Conda or the built-in Python venv.

Option A: Conda

conda env create -f practical_rl.yml
conda activate prac_rl

Option B: Python venv (Windows and macOS/Linux)

macOS/Linux:

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Optional: make the venv available in Jupyter
python -m ipykernel install --user --name chemengrl-venv --display-name "Python (chemengRL)"

Windows (PowerShell):

py -3 -m venv .venv
.venv\Scripts\Activate.ps1
pip install -r requirements.txt

# Optional: register the kernel for Jupyter
python -m ipykernel install --user --name chemengrl-venv --display-name "Python (chemengRL)"

Windows (Cmd):

py -3 -m venv .venv
.venv\Scripts\activate.bat
pip install -r requirements.txt

To deactivate the venv later, run deactivate.

Notebooks for Reactors

The notebooks for the discretised chemical reactor and the continuous reactor can be found in the src directory.

Start with the tabular setting (src/TabularQlearning.ipynb)first then move onto the more complex DQN notebook (src/DQN.ipynb).