Towards Global Explainability of Artificial Intelligence Agent Tactics in Close Air Combat

Emre Saldiran*, Mehmet Hasanzade, Gokhan Inalhan*, Antonios Tsourdos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In this paper, we explore the development of an explainability system for air combat agents trained with reinforcement learning, thus addressing a crucial need in the dynamic and complex realm of air combat. The safety-critical nature of air combat demands not only improved performance but also a deep understanding of artificial intelligence (AI) decision-making processes. Although AI has been applied significantly to air combat, a gap remains in comprehensively explaining an AI agent’s decisions, which is essential for their effective integration and for fostering trust in their actions. Our research involves the creation of an explainability system tailored for agents trained in an air combat environment. Using reinforcement learning, combined with a reward decomposition approach, the system clarifies the agent’s decision making in various tactical situations. This transparency allows for a nuanced understanding of the agent’s behavior, thereby uncovering their strategic preferences and operational patterns. The findings reveal that our system effectively identifies the strengths and weaknesses of an agent’s tactics in different air combat scenarios. This knowledge is essential for debugging and refining the agent’s performance and to ensure that AI agents operate optimally within their intended contexts. The insights gained from our study highlight the crucial role of explainability in improving the integration of AI technologies within air combat systems, thus facilitating more informed tactical decisions and potential advancements in air combat strategies.

Original languageEnglish
Article number415
Issue number6
Publication statusPublished - Jun 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 by the authors.


  • air combat
  • explainable
  • reinforcement learning
  • reward decomposition


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