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Unmanned combat air vehicle (UCAV) combat is a challenging scenario withcontinuous action space. In this paper, we propose a general hierarchicalframework to resolve the within-vision-range (WVR) air-to-air combat problemunder 6 dimensions of degree (6-DOF) dynamics. The core idea is to divide thewhole decision process into two loops and use reinforcement learning (RL) tosolve them separately. The outer loop takes into account the current combatsituation and decides the expected macro behavior of the aircraft according toa combat strategy. Then the inner loop tracks the macro behavior with a flightcontroller by calculating the actual input signals for the aircraft. We designthe Markov decision process for both the outer loop strategy and inner loopcontroller, and train them by proximal policy optimization (PPO) algorithm. Forthe inner loop controller, we design an effective reward function to accuratelytrack various macro behavior. For the outer loop strategy, we further adopt afictitious self-play mechanism to improve the combat performance by constantlycombating against the historical strategies. Experiment results show that theinner loop controller can achieve better tracking performance than fine-tunedPID controller, and the outer loop strategy can perform complex maneuvers toget higher and higher winning rate, with the generation evolves.

A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat