Artificial Time Dilation for Reinforcement Learning

A research project exploring the application of artificial time dilation concepts to accelerate reinforcement learning training through simulation speed scaling.

This project implements and demonstrates how velocity-based time dilation can be applied to AI agents in digital environments to accelerate breakthroughs. By scaling simulation speed (analogous to relativistic velocity), we can compress years of RL training into real-world hours.

• Time Dilation Simulation: Core algorithms for scaling simulation time vs real-world time
• MuJoCo Integration: RL environment support with configurable physics parameters
• Interactive Visualizations: D-scaling charts and reward curve analysis
• Research Paper: Complete academic paper with LaTeX formatting
• Web Demo: Interactive demonstration of time dilation effects
• Comprehensive Testing: Unit and integration tests for all components

├── src/                    # Source code
│   ├── core/              # Core time dilation algorithms
│   ├── visualization/     # Chart generation and plotting
│   ├── utils/             # Utility functions
│   └── config/            # Configuration management
├── docs/                  # Documentation
│   ├── paper/             # Research paper (LaTeX)
│   ├── api/               # API documentation
│   └── guides/            # User guides
├── data/                  # Data storage
│   ├── experiments/       # Experiment configurations
│   ├── results/           # Training results
│   └── models/            # Trained models
├── assets/                # Static assets
│   ├── images/            # Images and diagrams
│   └── charts/            # Generated charts
├── tests/                 # Test suite
│   ├── unit/              # Unit tests
│   └── integration/       # Integration tests
├── examples/              # Example scripts
├── demos/                 # Demo applications
└── web/                   # Web interface

1. Install Dependencies:

   pip install -r requirements.txt

2. Run Basic Example:

   python examples/basic_time_dilation.py

3. Launch Web Demo:

   python demos/web_demo.py

4. Generate Research Paper:

   cd docs/paper && latexmk -pdf main.tex

The dilation factor represents the ratio of simulated time steps to real-world time, scaling with computational speed:

• D = 1: Real-time simulation
• D = 100: 100x faster than real-time
• D = 1000: 1000x faster than real-time

Higher simulation speeds amplify effective training time, enabling RL agents to experience "centuries" of training in days.

• Accelerated RL Training: Compress years of training into hours
• Breakthrough Discovery: Enable rapid exploration of RL algorithms
• Resource Optimization: Maximize training efficiency
• Physics Simulation: Apply relativistic concepts to digital environments

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

MIT License - see LICENSE file for details

If you use this work in your research, please cite:

@article{artificial_time_dilation_rl,
  title={Artificial Time Dilation for Reinforcement Learning},
  author={[Your Name]},
  journal={[Journal Name]},
  year={2024}
}

For questions or collaboration, please open an issue or contact [your-email@domain.com].