  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.
