强化学习
模块化(生物学)
空战
人工智能
竞争对手分析
计算机科学
机器学习
运筹学
工程类
模拟
管理
遗传学
生物
经济
作者
Adrian P. Pope,Jaime S. Ide,Daria Micovic,Henry Díaz,David Rosenbluth,Lee Ritholtz,Jason C. Twedt,Thayne T. Walker,Kevin Alcedo,D. Javorsek
标识
DOI:10.1109/icuas51884.2021.9476700
摘要
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.
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