tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
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Merge branch 'dev' into docs/reward_and_mlp-design

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Tibo De Peuter 2026-04-09 15:32:10 +02:00 • committed by GitHub
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9
docs/CONTRIBUTING.md
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@ -33,3 +33,12 @@ Code readability is paramount, as code is read far more frequently than it is wr
* **Simulation:** The simulation environment utilizes a MuJoCo brittle star. XML MuJoCo structures must remain realistic and respect morphological constraints.
* **Experiment Tracking:** Weights & Biases (wandb) must be utilized for tracking and logging all experiments.
* **Code Styling:** All code must conform to the chosen style guide (Google standard). This is enforced via `uv` using **ruff** and pre-commit hooks.
## 5. AI-Assisted Development & Code Review
This project supports AI-assisted development to enhance productivity, but contributors must take full responsibility for all AI-generated outputs.
* **Self-Review Requirement:** Contributors must thoroughly self-review all AI-assisted code, documentation, and configurations before requesting peer review. This includes verifying correctness, adherence to project standards, scientific validity, and integration with existing code.
* **Quality Standards:** AI-generated content must meet the same rigorous standards as manually written code, including proper testing, documentation, and alignment with the scientific methodology outlined in Section 1.
* **Available Skills:** This project provides specific AI skills for common tasks (located in `.agents/skills/`), including linting and testing workflows. Contributors should leverage these skills to maintain consistency and quality.
* **Transparency:** When using AI assistance for complex algorithmic decisions or scientific design choices, contributors should document the rationale in commit messages or code comments where appropriate.

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docs/DEVELOPMENT.md
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@ -59,3 +59,25 @@ Verify your setup by running the JAX initialization test:
uv run pytest tests/test_jax_init.py
```
In the devcontainer, this will succeed on both CPU and GPU. A `GpuDevice` is expected if a GPU is detected and the `cuda` extra was installed.
## Logging & Monitoring
This project uses a unified logging system through the `experiment_logger` package. For a full API reference, see the [package README](../src/experiment_logger/README.md).
### Quick Setup
1. **Authorization**: Export your API key in your terminal to enable WandB synchronization:
```bash
export WANDB_API_KEY=your_copied_api_key_here
```
2. **Toggle Tracking**: Use the `--track` flag in `scripts/train.py` to enable online sync.
3. **Local Monitoring**: All runs are recorded in the `runs/` directory. View scalars with TensorBoard:
```bash
tensorboard --logdir runs/
```
### Environment Awareness
The logger automatically detects if it is running in an interactive terminal or a non-interactive environment (like an HPC Slurm job). It will automatically disable progress bars and switch to robust fallback modes (offline logging) to ensure your experiments never hang.