Optimal Stopping Problem

The optimal stopping problem focuses on determining the best time to take a specific action, given an evolving system and uncertain future outcomes. Current research emphasizes developing efficient algorithms, including reinforcement learning approaches and deep neural networks, to solve these problems, particularly in high-dimensional spaces where traditional methods struggle. This is driven by applications in diverse fields like finance (option pricing), causal inference, and resource management, where finding optimal stopping times can significantly improve decision-making and resource allocation. The development of robust and scalable solutions is a key focus, with recent work demonstrating the effectiveness of deep learning architectures in overcoming the "curse of dimensionality."