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arXiv · 2105.00990

Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials

Adrian P. Pope·Jaime S. Ide·Daria Micovic·Henry Diaz·David Rosenbluth·Lee Ritholtz·Jason C. Twedt·Thayne T. Walker·Kevin Alcedo·Daniel Javorsek
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Abstract

Autonomous control in high-dimensional, continuous state spaces is a persistent and important challenge in the fields of robotics and artificial intelligence. Because of high risk and complexity, the adoption of AI for autonomous combat systems has been a long-standing difficulty. In order to address these issues, DARPA's AlphaDogfight Trials (ADT) program sought to vet the feasibility of and increase trust in AI for autonomously piloting an F-16 in simulated air-to-air combat. Our submission to ADT solves the high-dimensional, continuous control problem using a novel hierarchical deep reinforcement learning approach consisting of a high-level policy selector and a set of separately trained low-level policies specialized for excelling in specific regions of the state space. Both levels of the hierarchy are trained using off-policy, maximum entropy methods with expert knowledge integrated through reward shaping. Our approach outperformed human expert pilots and achieved a second-place rank in the ADT championship event.

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Adrian P. Pope, Jaime S. Ide, Daria Micovic, Henry Diaz, David Rosenbluth, Lee Ritholtz, Jason C. Twedt, Thayne T. Walker, Kevin Alcedo, Daniel Javorsek. 2021-05-03. Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials. https://doi.org/10.1109/tai.2022.3222143

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