Abstract
Real-world problems often involve more than one
decision makers, each with their own goals or
preferences. While game theory is an established
paradigm for reasoning strategic interactions between multiple decision-makers, its applicability
in practice is often limited by the intractability of
computing equilibria in large games, and the fact
that the game parameters are sometimes unknown
and the players are often not perfectly rational. On
the other hand, machine learning and reinforcement
learning have led to huge successes in various domains and can be leveraged to overcome the limitations of the game-theoretic analysis. In this paper,
we introduce our work on integrating learning with
computational game theory for addressing societal
challenges such as security and sustainability