Asymptotic Nash Equilibria of Finite-State Ergodic Markovian Mean Field Games
研究了有限状态、无限时间且具有遍历成本的马尔可夫平均场博弈，证明了平均场博弈系统的解和主方程导出的策略都能构成n人博弈的ε-纳什均衡，并建立了渐近纳什均衡经验测度的大偏差原理。
Mean field games (MFGs) model equilibria in games with a continuum of weakly interacting players as limiting systems of symmetric n-player games. We consider the finite-state, infinite-horizon problem with ergodic cost. Assuming Markovian strategies, we first prove that any solution to the MFG system gives rise to a [Formula: see text]-Nash equilibrium in the n-player game. We follow this result by proving the same is true for the strategy profile derived from the master equation. We conclude the main theoretical portion of the paper by establishing a large deviation principle for empirical measures associated with the asymptotic Nash equilibria. Then, we contrast the asymptotic Nash equilibria using an example. We solve the MFG system directly and numerically solve the ergodic master equation by adapting the deep Galerkin method. We use these results to derive the strategies of the asymptotic Nash equilibria and compare them. Finally, we derive an explicit form for the rate functions in dimension two.