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# Brittle Star
> What is the impact of different levels of controller-modularity on the learning-speed, coordination and tolerance for
defects (e.g. amputations) in brittle-star-like robots trained with Reinforcement Learning?
## Quick start
### Local setup
To set up the UV module, you can run the following command:
```bash
uv sync --frozen
```
## Repository Structure
```text
.
├── configs/ # Hydra configuration files (YAML)
├── docs/ # Comprehensive documentation and API guides
├── runs/ # Default output directory for Hydra and training artifacts
├── scripts/ # High-level entrypoints for training, simulation, and evaluation
├── src/
│ └── brittle_star_project/ # Core library and environment logic
│ ├── evaluation/ # Checkpoint evaluation, rollout logic, and metrics persistence
│ └── trainers/ # Training loop implementations (e.g., PPO)
└── tests/ # Unit and integration tests
```
## Usage
For detailed instructions on how to use the project, please refer to the **[API Documentation](docs/README.md)**.
### Quick Start
1. **Train a model:**
```bash
uv run python scripts/train.py ppo.learning_rate=0.001 logging.track=true
```
2. **Monitor progress:**
See [Tracking & Monitoring](docs/api/tracking.md).
3. **Simulate a trained model:**
See [Simulation & Evaluation](docs/api/simulation.md).
## HPC
See **[docs/HPC.md](docs/HPC.md)** for the full guide, including environment setup, cluster selection, interactive debugging, and job submission.
## Documentation
Please find all documentation and a starting point for more information in [corresponding README](./docs/README.md).