Reinforcement Learning via Stochastic Control

Explore the intersection of reinforcement learning and stochastic control theory in this 39-minute conference talk from the Toronto Machine Learning Series. Presented by Professor Xunyu Zhou from Columbia University's Department of IEOR, delve into the importance of considering reinforcement learning in continuous time with continuous feature and action spaces. Discover how stochastic control theory provides a natural foundation for this approach, moving beyond the traditional Markov Decision Processes framework. Gain insights into the latest developments in this emerging field and learn about potential future research directions. Expand your understanding of reinforcement learning applications in complex, continuous environments and their theoretical underpinnings.