Abstract: Solving space differential game in an unknown environment, remains a challenging prob- lem. This paper proposes a pre-trained 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 pre-trained 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 environ- ment. Numerical experimental results validate the effectiveness of the proposed algorithms on improving the ability of the agents to adapt to the real environment. PDF
Keywords: Differential Games, Reinforcement Learning, Actor-Critic Learning, Fuzzy Systems