# 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
```

### Configuration

1. **Copy the default configuration:**
   ```bash
   cp configs/default_ppo.yaml configs/my_experiment.yaml
   ```

2. **Edit `configs/my_experiment.yaml`** to set your WandB credentials:
   ```yaml
   track: true  # Enable WandB logging
   wandb_entity: "your-wandb-username"  # Replace with your username/team
   wandb_project_name: "PPO-Modularity"
   ```

3. **(Optional) Login to WandB:**
   ```bash
   uv run wandb login
   ```

### Training

example command:

```bash
uv run python scripts/train.py
```

Or use a custom config file:

```bash
uv run python scripts/train.py --config configs/my_experiment.yaml
```

Override specific parameters:

```bash
uv run python scripts/train.py --learning-rate 0.001 --num-envs 32 --track
```

## 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).