A two stage learning technique using PSO-based FLC and QFIS for the pursuit evasion differential game

This paper presents a two stage learning technique that combines a particle swarm optimization (PSO)-based fuzzy logic control (FLC) algorithm with the Q-Learning fuzzy inference system (QFIS) algorithm. The PSO algorithm is used as a global optimizer to autonomously tune the parameters of a fuzzy logic controller. On the other hand, the QFIS algorithm is used as a local optimizer. We simulate mobile robots playing the differential form of the pursuit evasion game. The game is played such that the pursuer should learn its default control strategy on-line by interacting with the evader. We assume that the evader plays a well defined strategy which is to run away along the line of sight. The pursuer´s learning process depends on the rewards received from its environment. The proposed technique is compared through simulation with the default control strategy, the PSO-based fuzzy logic control algorithm, and the QFIS algorithm. Simulation results show that the proposed learning technique outperform the PSO-based fuzzy logic control algorithm and the QFIS algorithm with respect to the learning time which represents an important factor in on-line applications.