- Add run_dir and checkpoint_frequency to PPOArgs
- Update train.py to use run_dir for SummaryBoard, model saving, and loss plots
- Create configs/production_training.yaml for HPC production runs
- Update HPC.md with run_dir staging strategy details
Provide the features to read in Environment (Morphology, arena, ...) config files in the JSON format. The example JSON file contains the config for a brittle star with 2 arms
- Fix float32 serialization issue in unified logger metrics flushing
- Add jax.numpy import for proper type handling
- Add wandb/ directory to .gitignore to exclude temporary tracking files
- Tested wandb integration: metrics, artifacts, and local backup working correctly
- Replace direct WandB calls with experiment_logger.UnifiedLogger
- Replace all print() calls with proper logging framework
- Add automatic checkpoint saving every N iterations
- Configure root logger with proper format and level
- Maintain backward compatibility with TensorBoard writer
- Save final model with metadata using unified logger
Create reusable experiment logging package with:
- UnifiedLogger for multi-backend logging (WandB, disk, stdout)
- Automatic checkpoint and model saving with metadata
- WandB artifact upload support
- Graceful degradation when WandB unavailable
- Comprehensive API documentation
This is a standalone, project-agnostic library that can be reused
across different ML projects.
