论文标题
部分观察到的离散时间对风险敏感的平均野外游戏
Partially Observed Discrete-Time Risk-Sensitive Mean Field Games
论文作者
论文摘要
在本文中，我们考虑具有风险敏感的最佳标准的离散时间部分观察到的平均场游戏。我们通过指数效用函数为每个代理引入风险敏感性行为。在游戏模型中，通过国家的经验分布，每个人都通过其个人成本和状态动态来薄弱地与其他人群的其他人群相结合。我们使用将基本原始观察到的随机控制问题转换为在信仰空间和动态编程原理上的完全观察到的基础原始观察到的原始原始的技术的技术中，在无限居民群中建立了平均场平衡。然后，我们表明，在每个代理商采用的情况下，平均场均衡政策对于具有足够多代理的游戏形成了近似的NASH平衡。我们首先考虑有限的 - 摩恩斯成本函数，然后在本文的近距离部分中讨论结果扩展到无限 - 摩尼子成本。
In this paper, we consider discrete-time partially observed mean-field games with the risk-sensitive optimality criterion. We introduce risk-sensitivity behaviour for each agent via an exponential utility function. In the game model, each agent is weakly coupled with the rest of the population through its individual cost and state dynamics via the empirical distribution of states. We establish the mean-field equilibrium in the infinite-population limit using the technique of converting the underlying original partially observed stochastic control problem to a fully observed one on the belief space and the dynamic programming principle. Then, we show that the mean-field equilibrium policy, when adopted by each agent, forms an approximate Nash equilibrium for games with sufficiently many agents. We first consider finite-horizon cost function, and then, discuss extension of the result to infinite-horizon cost in the next-to-last section of the paper.
