Скачать или смотреть "Accelerating Optimization and Reinforcement Learning with Quasi-Stochastic Approx." by Sean Meyn

Sean Meyn (University of Florida) - Accelerating Optimization and Reinforcement Learning with Quasi-Stochastic Approximation

Talk abstract: The ODE method has been a workhorse for algorithm design and analysis since the introduction of the stochastic approximation. It is now understood that convergence theory amounts to establishing robustness of Euler approximations for ODEs, while theory of rates of convergence requires finer analysis. This talk concerns a parallel theory for quasi-stochastic approximation, based on algorithms in which the “noise” is based on deterministic signals. Part of the motivation is pedagogical: theory for convergence and convergence rates is greatly simplified. The other major motivation is practical: the speed of convergence is remarkably fast when compared to its stochastic counterparts. The talk will survey recent theory and applications to gradient free optimization and reinforcement learning.