Population Based Training

Population-based training (PBT) is a hyperparameter optimization technique that leverages a population of concurrently trained models to improve learning efficiency and performance in machine learning, particularly reinforcement learning. Current research focuses on enhancing PBT's adaptability and exploration capabilities through modifications like generalized PBT, incorporating diverse optimizers (e.g., combining first and second-order methods), and applying it to multi-objective optimization problems and neural architecture search. PBT's impact stems from its ability to improve the robustness and efficiency of training complex models, finding applications in diverse fields such as robotics, game playing, and resource allocation in cloud services.