Dataset-Driven Exploratory Deep Reinforcement Learning Using SINDy-Based Data Augmentation from Limited Indoor Thermal Measurements
This study examines the feasibility of combining Sparse Identification of Nonlinear Dynamical Systems (SINDy)-based data augmentation with an Actor–Critic deep reinforcement learning (DRL) model in a dataset-driven exploratory policy-learning configuration under limited-data conditions. The modeling and training datase...