Exploratory LQG Mean Field Games with Entropy Regularization
This work addresses the problem of finding optimal strategies in multi-agent systems for researchers in game theory and control, providing a theoretical extension to LQG MFGs.
This paper investigates entropy-regularized multi-variate LQG mean field games (MFGs) with K distinct sub-populations, extending actions to action distributions. The authors explicitly derive optimal action distributions for individual agents in the limiting MFG, showing they yield an ε-Nash equilibrium for the finite-population entropy-regularized MFG.
We study a general class of entropy-regularized multi-variate LQG mean field games (MFGs) in continuous time with $K$ distinct sub-population of agents. We extend the notion of actions to action distributions (exploratory actions), and explicitly derive the optimal action distributions for individual agents in the limiting MFG. We demonstrate that the optimal set of action distributions yields an $ε$-Nash equilibrium for the finite-population entropy-regularized MFG. Furthermore, we compare the resulting solutions with those of classical LQG MFGs and establish the equivalence of their existence.