Rethinking the Theoretical Foundation of Reinforcement Learning

Explore fundamental theoretical challenges in reinforcement learning through this 47-minute workshop presentation by Nan Jiang from the University of Illinois at Urbana-Champaign, delivered as part of the IFDS Workshop series at the Paul G. Allen School. Examine critical questions about the mathematical foundations underlying reinforcement learning algorithms and discover new theoretical perspectives that challenge conventional approaches to understanding how agents learn optimal behaviors through interaction with their environment. Gain insights into cutting-edge research that seeks to establish more robust theoretical frameworks for reinforcement learning, addressing limitations in current theoretical understanding and proposing alternative mathematical foundations that could lead to more effective learning algorithms and better performance guarantees.