- Submit all 10 full replication runs on accelerated partition
- Update experiment plan with complete validation results and full run status
- Add comprehensive full run scripts for robomimic and D3IL environments
- All validated environments now running full paper-quality experiments
- Total queue: 3 Gym + 4 Robomimic + 3 D3IL fine-tuning runs
- Complete validation status table with results for all environments
- Add WandB tracking URLs for completed fine-tuning runs
- Document technical fixes and current job queue status
- Add test scripts for remaining D3IL avoid_m3 and robomimic transport validation
- Complete SLURM test scripts for all environment types
- Gym fine-tuning: walker2d, halfcheetah validation tests
- Robomimic fine-tuning: lift validation test with scheduler fix
- D3IL validation: avoid_m1 pre-training and fine-tuning tests
- Updated experiment plan with current validation status
- All major environments now have automated testing pipeline
- Simplify experiment plan with clear phases and current status
- Add complete MuJoCo setup instructions for fine-tuning
- Update install script to include all dependencies
- Document current validation progress and next steps
- Updated all WandB project names to use dppo- prefix for organization
- Added flexible dev testing script for all environments
- Created organized dev_tests directory for test scripts
- Fixed MuJoCo compilation issues (added GCC compiler flags)
- Documented Python 3.10 compatibility and Furniture-Bench limitation
- Validated pre-training for Gym, Robomimic, D3IL environments
- Updated experiment tracking with validation results
- Enhanced README with troubleshooting and setup instructions
- Pre-training: diffusion model on offline D4RL data (200 epochs)
- Fine-tuning: PPO fine-tune with online environment interaction
- Dev test: 2 epochs only for quick verification, not full training
- Disable WandB in dev script to avoid config object vs string error
- Successfully completed development test (Job 3445106)
- Confirmed: pre-training works, loss reduces, checkpoints save
- Update experiment tracking with successful results
