An algorithm of pretrained fuzzy actor–critic learning applying in fixed-time space differential game

Abstract

Solving space differential game in an unknown environment remains a challenging problem. This article proposes a pretrained fuzzy actor–critic learning algorithm for dealing with the space pursuit-evasion game in fixed time. It is supposed that the research objects are two agents including one pursuer and one evader in space. A virtual environment, which is defined as the known part of the real environment, is utilized for deriving optimal strategies of the pursuer and the evader, respectively. Through employing the fuzzy inference system, a pretrained process, which is based on the genetic algorithm, is designed to obtain the initial consequent set of the pursuer and the evader. Besides, an actor–critic framework is applied to finely learn the suitable consequent set of the pursuer and evader in the real environment. Numerical experimental results validate the effectiveness of the proposed algorithms on improving the ability of the agents to adapt to the real environment.