Fixed-point Error Bounds for Mean-payoff Markov Decision Processes
9A Google TechTalks, presented by Roberto Cominneti, 2024-03-19
A Google Algorithms Seminar. ABSTRACT: We discuss the use of optimal transport techniques to derive finite-time error bounds for reinforcement learning in mean-payoff Markov decision processes. The results are obtained as a special case of stochastic Krasnoselski—Mann fixed point iterations for nonexpansive maps. We present sufficient conditions on the stochastic noise and stepsizes that guarantee almost sure convergence of the iterates towards a fixed point, as well as non-asymptotic error bounds and convergence rates. Our main results concern the case of a martingale difference noise with variances that can possibly grow unbounded. We also analyze the case of uniformly bounded variances, and how they apply for Stochastic Gradient Descent in convex optimization.
ABOUT THE SPEAKER: Roberto Cominetti is a professor with the Faculty of Engineering and Sciences at Universidad Adolfo Ibáñez, Santiago, Chile. His research interests include convex analysis and game theory and their applications in transportation networks.