UAV Air Game Maneuver Decision-Making Using Dueling Double Deep Q Network with Expert Experience Storage Mechanism

Deep reinforcement learning technology applied to three-dimensional Unmanned Aerial Vehicle (UAV) air game maneuver decision-making often results in low utilization efficiency of training data and algorithm convergence difficulties. To address these issues, this study proposes an expert experience storage mechanism that improves the algorithm’s performance with less experience replay time. Based on this mechanism, a maneuver decision algorithm using the Dueling Double Deep Q Network is introduced. Simulation experiments demonstrate that the proposed mechanism significantly enhances the algorithm’s performance by reducing the experience by 81.3% compared to the prioritized experience replay mechanism, enabling the UAV agent to achieve a higher maximum average reward value. The experimental results suggest that the proposed expert experience storage mechanism improves the algorithm’s performance with less experience replay time. Additionally, the proposed maneuver decision algorithm identifies the optimal policy for attacking target UAVs using different fixed strategies.