2026SEL3-project-Brittle_Star_Controller_Modularity/configs

Configuration Files

This directory contains configuration files for training experiments.

Quick Start

1. Choose a Template

For Development/Testing:

cp configs/dev_test.yaml configs/my_dev.yaml

For Production Training:

cp configs/production_training.yaml configs/my_experiment.yaml

2. Configure Your Settings

Edit your config file and set your wandb entity:

# ⚠️  IMPORTANT: Set this to your WandB username or team name
wandb_entity: "your-wandb-username"  
track: true  # Enable WandB logging

3. Run Training

Using config file:

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

Override specific parameters:

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

Pure CLI (no config file):

python src/train.py --track --wandb-entity your-username --total-timesteps 1000000

Features

📊 WandB Integration

• Real-time metrics logging
• Model checkpoints as artifacts
• Run comparison and collaboration

🔧 Flexible Configuration

• YAML files for reproducible experiments
• CLI overrides for quick adjustments
• Team collaboration without code changes

Configuration Templates

dev_test.yaml

• Fast iteration for development
• Short runs (100K timesteps)
• Frequent checkpoints
• Small environment count

production_training.yaml

• Full-scale training (50M timesteps)
• Optimized hyperparameters
• Production-ready settings

default_ppo.yaml

• Baseline configuration template
• Balanced settings for most use cases

Team Collaboration

Each team member should create their own config file:

# configs/alice_experiment.yaml
exp_name: "alice_locomotion_v2"
track: true
wandb_project_name: "PPO-Modularity"
wandb_entity: "alice-research"  # Alice's WandB username
total_timesteps: 20000000
# ... other settings

This allows everyone to:

• Use their own WandB account
• Run different experiments simultaneously
• Share configurations via version control
• Avoid conflicts in run names