[Statlist] Reminder: ETH Young Data Science Researcher Seminar Zurich, Virtual Seminar by Chi Jin, Princeton University, 3 July 2020

[Maurer Letizia]

Dear all

We are glad to announce the following talk in the virtual ETH Young Data Science Researcher Seminar Zurich

"Near-Optimal Reinforcement Learning with Self-Play"  
by Chi Jin, Princeton University

Time: Friday, 3 July 2020, 15:00-​16:00
Place: Zoom at https://ethz.zoom.us/j/92367940258

Abstract: Self-play, where the algorithm learns by playing against itself without requiring any direct supervision, has become the new weapon in modern Reinforcement Learning (RL) for achieving superhuman performance in practice. However, the majority of existing theory in reinforcement learning only applies to the setting where a single agent plays against a fixed environment. It remains largely open how to design efficient self-play algorithms in two-player sequential games, especially when it is necessary to manage the exploration/exploitation tradeoff. In this talk, we present the first line of provably efficient self-play algorithms in a basic setting of tabular episodic Markov games. Our algorithms further feature the near-optimal sample complexity---the number of samples required by our algorithms matches the information-theoretic lower bound up to a polynomial factor of the length of each episode.Best wishes,

M. Löffler, A. Taeb, Y. Chen

Seminar website: https://math.ethz.ch/sfs/news-and-events/young-data-science.html