Contributing

Thank you for your interest in contributing to ApexRL! This guide will help you get started.

Development Setup

  1. Fork and clone the repository:

git clone https://github.com/YOUR_USERNAME/Apex_rl.git
cd Apex_rl

  1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

  1. Install in development mode:

pip install -e .

Code Style

We use several tools to maintain code quality:

  • Black: Code formatting

  • Ruff: Linting

  • MyPy: Type checking

Format your code before committing:

black src/
ruff check src/
mypy src/

Pre-commit hooks are configured:

pre-commit install
pre-commit run --all-files

Making Changes

  1. Create a branch:

git checkout -b feature/your-feature-name

  1. Make your changes with appropriate tests

  2. Run tests:

pytest

  1. Update documentation if needed

  2. Commit your changes:

git add .
git commit -m "feat: add your feature"

Commit Message Format

We follow conventional commits:

  • feat: New feature

  • fix: Bug fix

  • docs: Documentation changes

  • style: Code style changes

  • refactor: Code refactoring

  • test: Test changes

  • chore: Maintenance tasks

Pull Request Process

  1. Update documentation for any new features

  2. Add tests for new functionality

  3. Ensure all tests pass

  4. Update CHANGELOG.md

  5. Submit PR with clear description

Areas for Contribution

Algorithms

  • [x] DQN (Deep Q-Network)

  • [x] SAC (Soft Actor-Critic)

  • [ ] TD3 (Twin Delayed DDPG)

  • [ ] A3C (Asynchronous Advantage Actor-Critic)

Networks

  • [ ] Transformer-based actors/critics

  • [ ] Graph Neural Networks

  • [ ] More CNN architectures

Features

  • [ ] Observation normalization

  • [ ] Reward normalization

  • [ ] Automatic hyperparameter tuning

  • [ ] Distributed training support

Documentation

  • [ ] More tutorials

  • [ ] Video tutorials

  • [ ] Example projects

  • [ ] API examples

Reporting Issues

When reporting issues, please include:

  1. Description of the issue

  2. Steps to reproduce

  3. Expected vs actual behavior

  4. Environment info (Python version, OS, GPU)

  5. Error messages or stack traces

Code of Conduct

  • Be respectful and inclusive

  • Welcome newcomers

  • Focus on constructive feedback

  • Respect differing viewpoints

License

By contributing, you agree that your contributions will be licensed under the Apache 2.0 License.

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