Hyperparameter Landscape

Hyperparameter optimization (HPO) focuses on efficiently finding the best settings for machine learning models, significantly impacting their performance and reproducibility. Current research emphasizes characterizing the complex "landscapes" of these hyperparameters—the relationship between parameter settings and model performance—across various machine learning domains, including reinforcement learning (RL) algorithms like DQN, PPO, and SAC. This involves developing benchmarks for comparing HPO methods and employing exploratory landscape analysis to understand the structure of these optimization problems, ultimately aiming to improve the efficiency and reliability of training machine learning models.