Tibo De Peuter
2d43f5e642
Co-authored-by: RobinMeersman <77965843+RobinMeersman@users.noreply.github.com> Co-authored-by: Tibo De Peuter <tibo.depeuter@telenet.be>
63 lines
1.3 KiB
Markdown
63 lines
1.3 KiB
Markdown
# Brittle Star
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## Quick Start
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### Installation
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To set up the UV module, you can run the following command:
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```bash
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uv sync --frozen
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```
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### Configuration
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1. **Copy the default configuration:**
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```bash
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cp configs/default_ppo.yaml configs/my_experiment.yaml
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```
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2. **Edit `configs/my_experiment.yaml`** to set your WandB credentials:
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```yaml
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track: true # Enable WandB logging
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wandb_entity: "your-wandb-username" # Replace with your username/team
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wandb_project_name: "PPO-Modularity"
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```
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3. **(Optional) Login to WandB:**
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```bash
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uv run wandb login
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```
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### Training
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example command:
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```bash
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uv run python scripts/train.py
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```
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Or use a custom config file:
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```bash
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uv run python scripts/train.py --config configs/my_experiment.yaml
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```
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Override specific parameters:
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```bash
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uv run python scripts/train.py --learning-rate 0.001 --num-envs 32 --track
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```
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### Logging
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The training script uses a unified logging framework that:
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- Logs to **WandB** (when enabled)
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- Saves metrics to **local disk** (JSON files in `runs/`)
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- Displays progress in **stdout**
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All experiment data is preserved locally, even if WandB is unavailable.
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## HPC
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See **[docs/HPC.md](docs/HPC.md)** for the full guide, including environment setup, cluster selection, interactive debugging, and job submission.
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