论文标题
具有有限状态和动作空间的平均现场游戏的近视调整过程
A Myopic Adjustment Process for Mean Field Games with Finite State and Action Space
论文作者
论文摘要
在本文中，我们介绍了具有有限状态和动作空间的平均现场游戏的自然学习规则，即所谓的近视调整过程。这些考虑因素的主要动机是确定动态平均场平衡所需的复杂计算，这使得代理是否确实能够发挥这些平衡似乎值得怀疑。我们证明，在相当广泛的条件下，近视调整过程通过确定性平衡策略在本地趋于固定平衡。此外，对于两局环境，我们还在更强但直观的条件下获得了全球收敛结果。
In this paper, we introduce a natural learning rule for mean field games with finite state and action space, the so-called myopic adjustment process. The main motivation for these considerations are the complex computations necessary to determine dynamic mean-field equilibria, which make it seem questionable whether agents are indeed able to play these equilibria. We prove that the myopic adjustment process converges locally towards stationary equilibria with deterministic equilibrium strategies under rather broad conditions. Moreover, for a two-strategy setting, we also obtain a global convergence result under stronger, yet intuitive conditions.
