2026SEL3-project-Brittle_Star_Controller_Modularity/docs/api/training.md

Training Models

This guide covers how to configure and run training experiments for the Brittle Star project using Hydra-based configurations.

Configuration

The project uses a modular configuration system powered by Hydra. Instead of passing many command-line flags, you select and override configuration groups.

Creating a Custom Experiment

1. Create a new experiment file: Create a file at configs/experiment/my_experiment.yaml. You can copy an existing one as a template:

   cp configs/experiment/base.yaml configs/experiment/my_experiment.yaml
   

2. Edit configs/experiment/my_experiment.yaml to set your experiment parameters:

   # @package _global_
   experiment:
     exp_name: "my_custom_run"
     seed: 42
   

Training Execution

To start a training run with the default settings defined in configs/main_config.yaml:

uv run python scripts/train.py

Using a Custom Experiment Configuration

To run with your custom experiment file:

uv run python scripts/train.py experiment=my_experiment

uv run python scripts/train.py ppo.learning_rate=0.001 ppo.num_envs=32 logging.track=true

Evaluation During Training

By default, the trainer saves checkpoints but does not evaluate them. To enable automatic headless evaluation of every saved checkpoint, set evaluation.evaluate_checkpoints=true:

uv run python scripts/train.py evaluation.evaluate_checkpoints=true

For more details on evaluation metrics and comparison tools, see Evaluation.

For more details on tracking your experiments, see Tracking & Monitoring.