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Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics

arXiv 2505.11311 ↗First posted 16 May 2025cs.MA cs.AI cs.LGcited by 0

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Abstract (arXiv)

Artificial intelligence (AI) is reshaping strategic planning, with Multi-Agent Reinforcement Learning (MARL) enabling coordination among autonomous agents in complex scenarios. However, its practical deployment in sensitive military contexts is constrained by the lack of explainability, which is an essential factor for trust, safety, and alignment with human strategies. This work reviews and assesses current advances in explainability methods for MARL with a focus on simulated air combat scenarios. We proceed by adapting various explainability techniques to different aerial combat scenarios to gain explanatory insights about the model behavior. By linking AI-generated tactics with human-understandable reasoning, we emphasize the need for transparency to ensure reliable deployment and meaningful human-machine interaction. By illuminating the crucial importance of explainability in advancing MARL for operational defense, our work supports not only strategic planning but also the training of military personnel with insightful and comprehensible analyses.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 33 references that are in the map. First posted on arXiv 16 May 2025.

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References · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  4. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
  5. 2021A Survey on Interpretable Reinforcement Learningcited by 73
  6. 2023A Review of Cooperation in Multi-agent Learningcited by 29
  7. 2019AI for Explaining Decisions in Multi-Agent Environmentscited by 12
  8. 2022Explainability in reinforcement learning: perspective and positioncited by 11
  9. 2023Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learningcited by 10
  10. 2023Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuveringcited by 5
  11. 2024A survey of air combat behavior modeling using machine learningcited by 3

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