Skip to content

CONTRIBUTING.md

Latest commit

 

History

History
57 lines (41 loc) · 2.32 KB

CONTRIBUTING.md

File metadata and controls

57 lines (41 loc) · 2.32 KB
Raw

Contributing to Rich Learning

Thank you for your interest in contributing! Rich Learning is a C#/.NET framework for continual reinforcement learning with topological graph memory.

How to Contribute

Areas We Need Help

  1. New Domain Encoders — Implement IStateEncoder for robotics, NLP, game AI, finance, etc.
  2. Alternative Graph Backends — Implement IGraphMemory with SQLite, Redis, in-memory, or other databases.
  3. New PoC Experiments — Demonstrate continual learning on your domain. Real data is strongly preferred.
  4. Performance Optimizations — SIMD intrinsics, Span usage, GPU acceleration, parallel processing.
  5. Documentation & Tutorials — Help others understand and use the framework.
  6. Bug Fixes — Found an issue? Fix it and send a PR.

Getting Started

  1. Fork the repo and clone your fork.
  2. Install .NET 10 SDK and Neo4j.
  3. Run the tests: dotnet build && dotnet run -- SplitMnist
  4. Create a feature branch: git checkout -b feature/my-awesome-encoder
  5. Make your changes with clear commit messages.
  6. Submit a Pull Request.

Code Standards

  • C# 12+ with nullable reference types enabled.
  • async/await for all I/O operations (Neo4j, file, network).
  • XML doc comments on all public APIs.
  • Records for immutable data models.
  • Interfaces for extensibility points (IStateEncoder, IGraphMemory, etc.).
  • No external ML frameworks — pure C# numerical code for core components.

Adding a New PoC

  1. Create a folder under src/RichLearning/PoC/YourDomain/
  2. Implement a domain-specific IStateEncoder
  3. Create a demo class with a RunAsync method
  4. Use real data — download datasets or include small test datasets
  5. Compare against at least one baseline (bare neural net, EWC, etc.)
  6. Wire it up in Program.cs

Pull Request Checklist

  • Code builds without errors: dotnet build
  • New code has XML doc comments
  • No new warnings introduced
  • PR description explains what and why
  • Real data used (not simulated/hardcoded) for PoCs

Code of Conduct

Be respectful, constructive, and welcoming. We're here to advance the science of continual learning together.

Questions?

Open an issue or start a discussion. We're happy to help you get started!