Approximate Dynamic Programming

Approximate Dynamic Programming (ADP) tackles complex sequential decision-making problems by approximating optimal solutions to dynamic programming formulations. Current research emphasizes developing efficient algorithms, such as those incorporating neural networks (e.g., NeurADP) or leveraging problem structure (e.g., "freezing" slow states), to handle high-dimensional state and action spaces. These advancements are improving the applicability of ADP across diverse fields, including resource allocation, control systems, and even game playing and bug triage, by enabling more accurate and computationally tractable solutions to previously intractable problems.