Merge branch 'dev' into docs/reward_and_mlp-design

README.md

@@ -3,7 +3,7 @@
 > What is the impact of different levels of controller-modularity on the learning-speed, coordination and tolerance for
 defects (e.g. amputations) in brittle-star-like robots trained with Reinforcement Learning?
 
-## Usage
+## Quick start
 
 ### Local setup
 
@@ -13,13 +13,46 @@ To set up the UV module, you can run the following command:
 uv sync --frozen
 ```
 
+### Configuration
+
+1. **Copy the default configuration:**
+   ```bash
+   cp configs/default_ppo.yaml configs/my_experiment.yaml
+   ```
+
+2. **Edit `configs/my_experiment.yaml`** to set your WandB credentials:
+   ```yaml
+   track: true  # Enable WandB logging
+   wandb_entity: "your-wandb-username"  # Replace with your username/team
+   wandb_project_name: "PPO-Modularity"
+   ```
+
+3. **(Optional) Login to WandB:**
+   ```bash
+   uv run wandb login
+   ```
+
+### Training
+
 example command:
 
 ```bash
-uv run src/train.py --model_name my_model --epochs 50 --batch_size 32
+uv run python scripts/train.py
 ```
 
-### HPC setup
+Or use a custom config file:
+
+```bash
+uv run python scripts/train.py --config configs/my_experiment.yaml
+```
+
+Override specific parameters:
+
+```bash
+uv run python scripts/train.py --learning-rate 0.001 --num-envs 32 --track
+```
+
+## HPC
 
 See **[docs/HPC.md](docs/HPC.md)** for the full guide, including environment setup, cluster selection, interactive debugging, and job submission.