tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
Archived
1
Fork 0
Code Issues Releases Activity

docs: reproduce results

Browse source
This commit is contained in:
Tibo De Peuter 2026-05-20 14:42:13 +02:00
parent 1ca6dd3129
commit 4fb21bbbb7
4 changed files with 114 additions and 22 deletions
Download patch file Download diff file Expand all files Collapse all files

22
docs/api/training.md
View file

@ -53,27 +53,7 @@ uv run python scripts/train.py evaluation.evaluate_checkpoints=true
## Reproducing Experiments
To ensure scientific validity and allow other researchers to reproduce your training runs, follow these steps:
1. **Lock Environment Dependencies**:
Always use the exact environment lockfile when running experiments. Run:
```bash
uv sync --frozen
```
This guarantees that the same package versions (including JAX, Flax, and MuJoCo) are used.
2. **Save and Locate Configuration Metadata**:
Every time you start a training run, the configuration is fully resolved by Hydra and saved as a metadata YAML file:
- For checkpointed steps: `runs/<run_dir>/checkpoints/<prefix>_step_<step>_metadata.yaml`
- For the final model: `runs/<run_dir>/final_model_metadata.yaml`
This metadata file contains every active hyperparameter (e.g., learning rate, morphology configuration, PPO parameters, etc.) for that specific run.
3. **Re-Run with Pinning**:
To reproduce a run, execute the training script with the configuration parameters specified in the metadata file, making sure to reuse the same seed:
```bash
uv run python scripts/train.py experiment=my_experiment ppo.learning_rate=0.001 experiment.seed=42
```
For detailed steps on how to reproduce training runs, locate run configuration metadata, or reproduce our experiments using Weights & Biases (WandB), see the **[Results & Reproduction Guide](../reproduction.md)**.
---