Simulation Methodology - An Overview

Explore the fundamentals of simulation methodology in this comprehensive lecture from the Theory of Reinforcement Learning Boot Camp. Delve into key concepts such as numerical integration, multiple integrals, high-dimensional integrals, and the Monte Carlo method. Learn about the Central Limit Theorem, Monte Carlo integration, and quasi-random integration techniques. Examine output analysis methods, including fixed sample size and smooth functions of expectations. Investigate non-regular inference and subsampling approaches. Gain insights into the challenges faced in massively parallel computing environments. Presented by Peter Glynn from Stanford University, this talk provides a thorough overview of simulation methodology, equipping learners with essential knowledge for advanced computational techniques in reinforcement learning and related fields.