PyRL

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Exploring Reinforcement Learning in Python

Features: - Write reinforcement learning algorithms in python - Train RL models to use with current RL python packages - Tune hyperparameters using Optuna - Use wrappers to track model performance - Create custom RL environments to use with RL and MARL packages

Documentation

The documentation is built automatically using Sphinx and deployed to GitHub Pages. You can find the latest documentation at:

https://bwhewe-13.github.io/pyrl/

To build the documentation locally:

# Install dependencies
python -m pip install -r docs/requirements.txt

# Build HTML docs
cd docs
make html
# Output will be in docs/build/html

Development

To set up the development environment:

# Clone the repository
git clone https://github.com/bwhewe-13/pyrl.git
cd pyrl

# Install package with development dependencies
python -m pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Running Tests

To run the tests with coverage reporting:

pytest

Coverage reports will be generated in coverage_html/index.html and coverage.xml.