Enhanced Mobility Robustness Optimization (MRO) Module

[WaterMenon09]

Enhanced Mobility Robustness Optimization (MRO) Module

Summary

Enhanced the Mobility Robustness Optimization (MRO) module with comprehensive improvements including a new Bayesian optimization approach, improved logging infrastructure, better documentation, and refined cell attachment logic.

Key Changes

• Added new BayesianMRO class implementing GPR and XGBoost optimization for hysteresis and TTT parameter tuning
• Replaced print statements with structured logging throughout the codebase
• Enhanced documentation with comprehensive docstrings across all MRO classes
• Improved error handling and GPU acceleration support
• Refined cell attachment logic with better hysteresis and TTT handling

Key Features

New Bayesian ML Optimization (mro_ml.py)

• Implements Bayesian optimization using Gaussian Process Regression (GPR) or XGBoost as surrogate models
• Appended Baysian optimization approach to the mro notebook for easy reproducability
• Uses Expected Improvement (EI) acquisition function for efficient parameter space exploration
• Supports both CPU and GPU acceleration via PyTorch
• Provides systematic approach to finding optimal hysteresis and Time-to-Trigger parameters

Enhanced MRO Base Class

• Logging Infrastructure: Replaced all print statements with structured logging using Python's logging module
• GPU Support: Added CUDA device detection and model placement for improved performance
• Better Error Handling: Enhanced exception handling with proper logging at different severity levels
• Improved Cell Update Logic: Simplified _update() method by removing unnecessary kernel reconfiguration and jitter adjustments

Reinforcement Learning MRO Improvements (mro_rl.py)

• Added comprehensive module-level and class-level documentation
• Enhanced docstrings explaining the RL approach to MRO optimization
• Improved simulation boundary detection using find_sim_boundary()
• Better integration with the RADP library utilities

Documentation Enhancements

• Added detailed docstrings for all public methods with parameter descriptions, return types, and examples
• Included table-formatted examples showing expected data structures
• Documented initialization parameters and their purposes
• Added explanations of optimization approaches and algorithms

Technical Details

Dependencies

• Added optional XGBoost support for Bayesian optimization
• Enhanced PyTorch integration for GPU acceleration

Testing

• Minor bug fixes for MRO testing suite
• All existing tests continue to pass

[WaterMenon09]

could you please just run the end-to-end-test again for it to pass?

[WaterMenon09]

Please make sure to reverse last Shah uncle's commit before merging