Seminar Series Archive

Potential games are arguably one of the most important and widely studied classes of normal form games. They define the archetypal setting of multi-agent coordination as all agent utilities are perfectly aligned with each other via a common potential function. Can this intuitive framework be transplanted in the setting of Markov Games? What are the similarities and differences between multi-agent coordination with and without state dependence? We present a novel definition of Markov Potential Games (MPG) that generalizes prior attempts at capturing complex stateful multi-agent coordination. Counter-intuitively, insights from normal-form potential games do not carry over as MPGs can consist of settings where state-games can be zero-sum games. In the opposite direction, Markov games where every state-game is a potential game are not necessarily MPGs. Nevertheless, MPGs showcase standard desirable properties such as the existence of deterministic Nash policies. In our main technical result, we prove fast convergence of independent policy gradient (and its stochastic variant) to Nash policies by adapting recent gradient dominance property arguments developed for single agent MDPs to multi-agent learning settings.
Joint work with Stefanos Leonardos, Will Overman and Georgios Piliouras.

Ioannis is an Assistant Professor of Computer Science at UCI. He is interested in the theory of computation, machine learning and its interface with non-convex optimization, dynamical systems, probability and statistics. Before joining UCI, he was an Assistant Professor at Singapore University of Technology and Design. Prior to that he was a MIT postdoctoral fellow working with Constantinos Daskalakis. 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 M.Sc. in Mathematics from Georgia Tech. He is the recipient of the 2019 NRF fellowship for AI.