Abstract:
In this talk, we will focus on learning in two-player games. First, we will provide a brief introduction to the possible behaviors of learning algorithms and mention various techniques that have been extensively used to guarantee convergence to Nash equilibria in zero-sum games. Finally, we will demonstrate how these techniques can be applied to learn Nash equilibria in rank-1 games and discuss their implications for general-sum games.
Based on joint works with Ioannis Anagnostides, Gabriele Farina, Nikolas Patris and Tuomas Sandholm
Bio:
Ioannis is an Assistant Professor of Computer Science at UC Irvine and a researcher at Archimedes AI. He is interested in the theory of computation, machine learning and its interface with non-convex optimization, dynamical systems, learning in games, statistics and multi-agent reinforcement learning. Before joining UCI, he was an Assistant Professor at Singapore University of Technology and Design. Prior to that he was a MIT postdoctoral fellow. He received his PhD in Algorithms, Combinatorics and Optimization from Georgia Tech in 2016, a Diploma in EECS from National Technical University of Athens, and a MS in Mathematics from Georgia Tech. He is the recipient of the 2019 NRF fellowship for AI.