This April 2025 paper introduces HyperController, a novel and computationally efficient algorithm designed to optimize hyperparameters during the training of reinforcement learning neural networks. Hyperparameter optimization is crucial for improving machine learning models, but traditional methods can be slow and computationally intensive. HyperController addresses these challenges by modeling the hyperparameter optimization problem as an unknown Linear Gaussian Dynamical System and leveraging the Kalman filter for efficient prediction. The algorithm is validated through experiments on various OpenAI Gymnasium environments, where it demonstrates faster training times and superior or comparable performance compared to existing methods, achieving the highest median reward in four out of five environments. The research highlights HyperController's potential for stable and efficient training of reinforcement learning neural networks, offering advantages like quicker deployment and easier fine-tuning.
Source:
https://arxiv.org/pdf/2504.19382
