refactor: set wandb entity and simplify READMEs

configs/README.md

@@ -2,91 +2,22 @@
 
 This directory contains configuration files for training experiments.
 
-## Quick Start
+## Usage
 
-### 1. Choose a Template
+Use `--config` with `src/train.py` to run an experiment:
 
-**For Development/Testing:**
 ```bash
-cp configs/dev_test.yaml configs/my_dev.yaml
+python src/train.py --config configs/default_ppo.yaml
 ```
 
-**For Production Training:**
+You can overriding settings via CLI:
 ```bash
-cp configs/production_training.yaml configs/my_experiment.yaml
+python src/train.py --config configs/default_ppo.yaml --learning-rate 0.001
 ```
 
-### 2. Configure Your Settings
+## Available Configurations
 
-Edit your config file and **set your wandb entity**:
+- `default_ppo.yaml`: Baseline config.
-```yaml
+- `dev_test.yaml`: Fast iteration for development.
-# ⚠️  IMPORTANT: Set this to your WandB username or team name
+- `production_training.yaml`: Full-scale training.
-wandb_entity: "your-wandb-username"  
+- `personal_template.yaml`: Template for team members to customize.
-track: true  # Enable WandB logging
-```
-
-### 3. Run Training
-
-**Using config file:**
-```bash
-python src/train.py --config configs/my_experiment.yaml
-```
-
-**Override specific parameters:**
-```bash
-python src/train.py --config configs/my_experiment.yaml --learning-rate 0.001 --num-envs 32
-```
-
-**Pure CLI (no config file):**
-```bash
-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:
-
-```yaml
-# 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

configs/default_ppo.yaml

@@ -15,7 +15,7 @@ seed: 1
 # Tracking settings
 track: false  # Set to true to enable WandB logging
 wandb_project_name: "PPO-Modularity"
-wandb_entity: null  # Set to your WandB username or team name
+wandb_entity: "SEL3-2026-Groep-4"  # Set to your WandB username or team name
 
 # Model saving
 save_model: true

configs/dev_test.yaml

@@ -9,7 +9,7 @@ seed: 123
 # Tracking settings - IMPORTANT: Set your own wandb_entity!
 track: true
 wandb_project_name: "PPO-Modularity-Dev"
-wandb_entity: null  # ⚠️  SET THIS TO YOUR WANDB USERNAME OR TEAM
+wandb_entity: "SEL3-2026-Groep-4"  # ⚠️  SET THIS TO YOUR WANDB USERNAME OR TEAM
 
 # Model saving
 save_model: true

configs/personal_template.yaml

@@ -9,7 +9,7 @@ seed: 42
 # WandB settings - ⚠️ IMPORTANT: Set your credentials!
 track: true  # Enable WandB tracking
 wandb_project_name: "PPO-Modularity"
-wandb_entity: "YOUR_WANDB_USERNAME"  # ⚠️ CHANGE THIS to your WandB username/team
+wandb_entity: "SEL3-2026-Groep-4"  # ⚠️ CHANGE THIS to your WandB username/team
 
 # Quick experiment settings (modify as needed)
 total_timesteps: 500000  # 500K for quick results

configs/production_training.yaml

@@ -10,7 +10,7 @@ seed: 42
 # Tracking settings - IMPORTANT: Set your own wandb_entity!
 track: true
 wandb_project_name: "PPO-Modularity"
-wandb_entity: null  # ⚠️  SET THIS TO YOUR WANDB USERNAME OR TEAM
+wandb_entity: "SEL3-2026-Groep-4"  # ⚠️  SET THIS TO YOUR WANDB USERNAME OR TEAM
 
 # Model saving
 save_model: true

src/brittle_star_project/dataclasses/PPOArgs.py

@@ -26,7 +26,7 @@ class PPOArgs:
     wandb_project_name: str = "PPO-Modularity"
 
     # the entity (team) of wandb's project
-    wandb_entity: str | None = None
+    wandb_entity: str | None = "SEL3-2026-Groep-4"
 
     # whether to capture videos of the agent performances (check out `videos` folder)
     capture_video: bool = False

src/experiment_logger/README.md

@@ -1,145 +1,17 @@
 # Experiment Logger
 
-A lightweight, standalone logging framework for machine learning experiments with multi-backend support.
+A lightweight logging framework supporting Weights & Biases, local JSON, and stdout.
 
-## Features
+## Usage
-
-- **Multi-backend logging**: Simultaneously log to WandB, local disk (JSON), and stdout
-- **Data preservation**: All metrics saved locally, even if WandB is unavailable
-- **Checkpoint management**: Save model checkpoints with metadata
-- **WandB integration**: Optional artifact upload for model versioning
-- **Graceful degradation**: Works without WandB installed
-- **Simple API**: Minimal configuration required
-
-## Installation
-
-This package is included in the project. To use it in your code:
-
-```python
-from experiment_logger import UnifiedLogger
-```
-
-## Quick Start
 
 ```python
 from experiment_logger import UnifiedLogger
 
-# Initialize logger
+logger = UnifiedLogger(run_name="my_experiment", config={"lr": 0.001})
-logger = UnifiedLogger(
-    run_name="my_experiment",
-    config={"learning_rate": 0.001, "batch_size": 32},
-    project_name="MyProject",
-    entity="my-wandb-username",  # Optional
-    use_wandb=True,  # Set to False to disable WandB
-)
 
-# Log metrics
+logger.log({"loss": 0.5}, step=1)
-for step in range(100):
+logger.save_checkpoint(params=model_params, step=1)
-    logger.log({
-        "loss": 1.0 / (step + 1),
-        "accuracy": step * 0.01,
-    }, step=step)
-
-# Save checkpoint
-logger.save_checkpoint(
-    params=model_params,
-    step=100,
-    metadata={"epoch": 1, "val_acc": 0.95},
-)
-
-# Save final model
-logger.save_final_model(
-    params=final_params,
-    metadata={"final_accuracy": 0.98},
-)
-
-# Finalize (flushes remaining metrics)
 logger.finish()
 ```
 
-## Context Manager
+Logs and checkoints are saved in the `runs/` directory. If `track=True` (or `use_wandb=True`), everything is additionally synced to Weights & Biases.
-
-Use as a context manager for automatic cleanup:
-
-```python
-with UnifiedLogger(run_name="my_exp", config={}) as logger:
-    logger.log({"metric": 1.0})
-    # Automatically calls finish() on exit
-```
-
-## Configuration
-
-### Constructor Parameters
-
-- `run_name` (str): Unique name for this run
-- `config` (dict): Configuration dictionary with hyperparameters
-- `project_name` (str): WandB project name (default: "PPO-Modularity")
-- `entity` (str, optional): WandB entity (team/user name)
-- `base_dir` (str): Base directory for local storage (default: "runs")
-- `use_wandb` (bool): Enable WandB logging (default: True)
-- `save_code` (bool): Save code to WandB (default: True)
-
-### Directory Structure
-
-```
-runs/
-└── my_experiment/
-    ├── config.json          # Saved configuration
-    ├── metrics/
-    │   └── metrics.jsonl    # Line-delimited JSON metrics
-    ├── checkpoints/
-    │   ├── checkpoint_step_100.flax
-    │   └── checkpoint_step_100_metadata.json
-    └── final_model.flax
-```
-
-## API Reference
-
-### `log(metrics, step=None, commit=True)`
-
-Log metrics to all backends.
-
-**Parameters:**
-- `metrics` (dict): Dictionary of metric name -> value
-- `step` (int, optional): Global step counter (auto-incremented if None)
-- `commit` (bool): Whether to commit to WandB immediately
-
-### `save_checkpoint(params, step, prefix="checkpoint", metadata=None)`
-
-Save model checkpoint to disk and optionally to WandB.
-
-**Parameters:**
-- `params`: Model parameters (Flax params or any serializable object)
-- `step` (int): Current training step
-- `prefix` (str): Prefix for checkpoint filename
-- `metadata` (dict, optional): Additional metadata to save
-
-### `save_final_model(params, metadata=None)`
-
-Save the final trained model.
-
-**Parameters:**
-- `params`: Model parameters
-- `metadata` (dict, optional): Metadata about the final model
-
-### `finish()`
-
-Finalize logging and cleanup. Flushes remaining metrics to disk.
-
-## Usage in Projects
-
-This logger is designed to be:
-- **Project-agnostic**: Use in any ML project, not just this one
-- **Framework-agnostic**: Works with JAX, PyTorch, TensorFlow, etc.
-- **Minimal dependencies**: Only requires `wandb` (optional), `flax` (for serialization), and `numpy`
-
-## Design Philosophy
-
-1. **Never lose data**: All metrics saved locally, regardless of WandB availability
-2. **Simple API**: Minimal boilerplate, easy to integrate
-3. **Fail gracefully**: Missing WandB shouldn't break experiments
-4. **Reproducibility**: Save full configuration with every run
-
-## License
-
-Part of the 2026SEL3-project-BrittleStar repository.