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arXiv 2008.13420math.OCecon.TH
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具有有限状态和行动空间的平均场对策的近视调整过程

A Myopic Adjustment Process for Mean Field Games with Finite State and Action Space

Berenice Anne Neumann

摘要:

在本文中,我们介绍了一个自然的学习规则的平均场游戏有限的状态和行动空间,所谓的近视调整过程。这些考虑的主要动机是确定动态平均场均衡所需的复杂计算,这使得代理人是否确实能够发挥这些均衡似乎值得怀疑。我们证明了近视的调整过程收敛局部平稳平衡与确定性的平衡策略,在相当广泛的条件下。此外,对于两个策略设置,我们也得到了一个整体收敛的结果下,更强,但直观的条件。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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