Artificial Intelligence (AI) is becoming a critical component in the defense
industry, as recently demonstrated by DARPA`s AlphaDogfight Trials (ADT). ADT
sought to vet the feasibility of AI algorithms capable of piloting an F-16 in
simulated air-to-air combat. As a participant in ADT, Lockheed Martin`s (LM)
approach combines a hierarchical architecture with maximum-entropy
reinforcement learning (RL), integrates expert knowledge through reward
shaping, and supports modularity of policies. This approach achieved a $2^{nd}$
place finish in the final ADT event (among eight total competitors) and
defeated a graduate of the US Air Force's (USAF) F-16 Weapons Instructor Course
in match play.

人工智能在国防工业中的应用越来越重要，Lockheed Martin 在参加 DARPA AlphaDogfight Trials 竞赛时，使用基于 Hierarchical 架构与最大熵强化学习（RL），通过奖励塑形整合专业知识，并支持策略的模块化方法，取得了第二名的好成绩，并在比赛中击败了美国空军 F-16 武器教官课程的毕业生。