Research on UAV Air Combat Maneuver Decision-making Based on Improved Q-learning Algorithm

Abstract

Aiming at the autonomous maneuver decision-making problem of UAV air combat, a lateral maneuver decision-making algorithm is designed. By adding heuristic factors and double Q-table alternating learning mechanism, the defects of slow learning speed and more ineffective learning in traditional Q-learning algorithm are improved. Through the comparison of path planning simulation and data, it is verified that the improved Q-learning algorithm has better stability and solving ability. it is verified that the improved Q-learning algorithm can play a significant role in improving the winning / losing ratio of UAV air combat through the exchange of weapon platforms.