UAV Air Combat Maneuver Decision Based on Improved Q-Learning Algorithm

For autonomous air combat maneuver decision problem of VAV, a Q-Learning (OL) decision algorithm is designed. To improve the flexibility of decision-making environment, a dynamic grid environment is designed, which can adaptively adjust the planning range and resolution. The path planning model is designed based on QL algorithm, and the algorithm is improved by using double Q-table learning mechanism, which effectively improves the quality of path planning. Based on the Nash equilibrium theory, the vertical maneuver algorithm model is constructed, and the cost calculation function is designed according to different situation environments, and the vertical maneuver decision of VAV is realized. Simulation has verified the effectiveness and feasibility of the algorithm.