tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity

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Tibo De Peuter f31436bccd feat(config): add YAML configuration system

- Add default_ppo.yaml template for training configurations
- Create configs/README.md with usage documentation
- Enable per-researcher configuration without code changes
- Support YAML config files with CLI parameter overrides
- Document how to set personal WandB credentials safely

This allows researchers to maintain personal configs without
committing credentials to the repository.

2026-03-31 19:53:15 +00:00

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Configuration Files

This directory contains configuration files for training experiments.

Usage

Configuration files use YAML format and allow you to specify all training parameters in one place.

Quick Start

Copy the default configuration template:

cp configs/default_ppo.yaml configs/my_experiment.yaml

Edit my_experiment.yaml to customize your experiment settings, particularly:

wandb_entity: Your WandB username or team name
track: Set to true to enable WandB logging
Training hyperparameters as needed

Run training with your config:

python src/train.py --config configs/my_experiment.yaml

Override Parameters

You can override any parameter from the command line:

python src/train.py --config configs/my_experiment.yaml --learning-rate 0.001 --num-envs 32

Configuration for Different Users

Each researcher should create their own config file with their WandB settings:

# configs/researcher_name.yaml
exp_name: "researcher_name_experiment"
track: true
wandb_project_name: "PPO-Modularity"
wandb_entity: "your-wandb-username"  # Change this!

This approach allows everyone to use the codebase without modifying source files.

Available Configurations

default_ppo.yaml - Default PPO training configuration template

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