Kalman fuzzy actor-critic learning automaton algorithm for the pursuit-evasion differential game

This paper presents a decentralized learning technique that enables two pursuers or more to capture a single evader in the pursuit-evasion (PE) differential games. Both the pursuers and the evader should learn their control strategies simultaneously by interacting with each other. The proposed learning technique uses a fuzzy actor-critic learning automaton (FACLA) algorithm together with the Kalman filter technique. Both the critique and the actor are fuzzy inference systems (FIS). The Kalman filter is used as an estimation method for the evader's next position. Then, the pursuers can find the evader's movement direction based on this estimation to avoid collision among them and to reduce the capture time. It is assumed that each pursuer knows only the instantaneous position of the evader and vice versa. Also, we assume that there is not any type of communication among them and each pursuer considers the other pursuers as part of its environment. So, the cooperation among pursuers to minimize the capture time can be done in a decentralized manner. For each player in the game, the proposed learning technique is used to autonomously tune the parameters of its fuzzy logic controller (FLC) to self-learn its control strategy. Simulation results for the case of three players PE differential game; two pursuers attempt to capture a single evader; are given to show the feasibility of our learning algorithm.