Interpretable DRL-based Maneuver Decision of UCAV Dogfight

This paper proposes a three-layer unmanned combat aerial vehicle (UCAV) dogfight frame where Deep reinforcement learning (DRL) is responsible for high-level maneuver decision.A four-channel low-level control law is firstly constructed, followed by a library containing eight basic flight maneuvers (BFMs).Double deep Q network (DDQN) is applied for BFM selection in UCAV dogfight, where the opponent strategy during the training process is constructed with DT.Our simulation result shows that, the agent can achieve a win rate of 85.75% against the DT strategy, and positive results when facing various unseen opponents.Based on the proposed frame, interpretability of the DRL-based dogfight is significantly improved.The agent performs yo-yo to adjust its turn rate and gain higher maneuverability.Emergence of "Dive and Chase" behavior also indicates the agent can generate a novel tactic that utilizes the drawback of its opponent.