Online Reinforcement Learning and Regret

Explore the intersection of online learning and reinforcement learning in this comprehensive tutorial presented by Christina Lee Yu and Sean Sinclair from Cornell University. Delve into the challenges of analyzing regret under Markovian dynamics when the model is unknown. Build upon concepts from multi-arm bandits to understand how actions selected during the learning process impact overall performance. Examine the interplay between learning and function approximation, investigate the importance of structure in reinforcement learning algorithms, and discover current challenges and open problems in the field. Gain valuable insights into data-driven decision processes and expand your understanding of online reinforcement learning techniques.

Online Reinforcement Learning and Regret