Learning Equilibria in Coordination Games via Minorization-Maximization
For game theory and multi-agent systems, it provides a novel method for selecting a unique equilibrium in regularized games with provable convergence guarantees.
The paper addresses equilibrium selection in coordination games with irrational agents, proposing a minorization-maximization learning scheme that converges to a potential-optimal equilibrium, outperforming gradient and best response methods in convergence behavior.
This paper considers games where the utilities for agents are the sum of a term proportional to a social utility, and another term that is an individual cost or reward. The agents are assumed to be irrational in their perception of the individual cost or reward. The multi equilibrium game is regularized, and its strictly concave potential function is used to select a unique equilibrium. This selected equilibrium is shown to be an $ε-$equilibrium of the original game, where $ε$ is parametrized by the regularizing function. A minorization-maximization based iterative learning scheme is proposed to learn equilibria in this game. This scheme converges to the potential-optimal equilibrium, and has superior convergence behaviour in comparison to gradient and best response methods.