最小代价微分对策的价值函数逼近

Pub Date : 2021-12-01 DOI:10.35634/vm210402
Y. Averboukh
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引用次数: 1

摘要

研究了代价最小的零和微分对策的值函数,即收益函数由轨迹上某个量的最小化决定的微分对策,用一个参与人控制的连续时间随机对策的解逼近。注意,辅助连续时间随机对策的值函数是用带有附加不等式约束的Isaacs-Bellman方程描述的。对于随机微分对策,Isaacs-Bellman方程是抛物型偏微分方程,对于连续时间马尔可夫对策,它是一种偏微分方程系统。本文提出的近似是基于Krasovskii和Kotelnikova首先提出的随机波导的概念。
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Approximation of value function of differential game with minimal cost
The paper is concerned with the approximation of the value function of the zero-sum differential game with the minimal cost, i.e., the differential game with the payoff functional determined by the minimization of some quantity along the trajectory by the solutions of continuous-time stochastic games with the stopping governed by one player. Notice that the value function of the auxiliary continuous-time stochastic game is described by the Isaacs–Bellman equation with additional inequality constraints. The Isaacs–Bellman equation is a parabolic PDE for the case of stochastic differential game and it takes a form of system of ODEs for the case of continuous-time Markov game. The approximation developed in the paper is based on the concept of the stochastic guide first proposed by Krasovskii and Kotelnikova.
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