Statistical Considerations in Reinforcement Learning - Part 1a

Statistical Considerations in Reinforcement Learning - Part 1a

Explore the foundational concepts of statistical considerations in reinforcement learning in this comprehensive lecture from the Theory of Reinforcement Learning Boot Camp. Delve into topics such as inverse reinforcement learning, high-stakes problems, and the Max of Means Problem. Examine unique maximizers, simulations, and uncertainty in reinforcement learning contexts. Investigate local parameter asymptotics, natural estimators, and the moving parameter asymptotic framework. Gain insights into projection intervals and conservative estimators as presented by Eric Laber from North Carolina State University in this 1-hour and 14-minute talk hosted by the Simons Institute.