Reinforcement Learning-Enhanced Gradient Boosting Machines

Author(s): Shenggang Li

Originally published on Towards AI.

A Novel Approach to Integrating Reinforcement Learning within Gradient Boosting Internal Optimization for Superior Predictive PerformancePhoto by Austin Neill on Unsplash

In this post, I demonstrate how reinforcement learning (RL) can directly enhance the performance of gradient boosting models (GBM) by dynamically adjusting the learning rate at each boosting iteration. Unlike traditional approaches — such as hyperparameter tuning, model blending, or architecture searches — my method integrates RL directly into the gradient boosting procedure. Specifically, the RL agent actively determines an optimal learning rate to scale the gradient updates of each new tree, adaptively controlling its contribution to the final model. This dynamic adjustment enables GBM to efficiently respond to evolving data patterns, significantly boosting performance.

To validate this approach, I conducted experiments using both synthetic and Kaggle benchmark datasets. For regression tasks, the RL-guided GBM consistently outperformed competitive models like XGBoost, LightGBM, and Random Forest. For classification tasks, the method achieved accuracy comparable to state-of-the-art models such as LightGBM and XGBoost, while clearly outperforming Random Forest and AdaBoost. I include detailed Python code demonstrating precisely how the RL agent selects optimal gradient scaling factors dynamically during training, ensuring each boosting iteration is strategically tailored to maximize predictive accuracy.

There is considerable potential to further refine this method. Future improvements could involve adopting more sophisticated RL algorithms or… Read the full blog for free on Medium.

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Published via Towards AI