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Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning

arXiv 2505.08995 ↗First posted 13 May 2025cs.AI cs.LG cs.MAcited by 0

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Abstract (arXiv)

This work presents a Hierarchical Multi-Agent Reinforcement Learning framework for analyzing simulated air combat scenarios involving heterogeneous agents. The objective is to identify effective Courses of Action that lead to mission success within preset simulations, thereby enabling the exploration of real-world defense scenarios at low cost and in a safe-to-fail setting. Applying deep Reinforcement Learning in this context poses specific challenges, such as complex flight dynamics, the exponential size of the state and action spaces in multi-agent systems, and the capability to integrate real-time control of individual units with look-ahead planning. To address these challenges, the decision-making process is split into two levels of abstraction: low-level policies control individual units, while a high-level commander policy issues macro commands aligned with the overall mission targets. This hierarchical structure facilitates the training process by exploiting policy symmetries of individual agents and by separating control from command tasks. The low-level policies are trained for individual combat control in a curriculum of increasing complexity. The high-level commander is then trained on mission targets given pre-trained control policies. The empirical validation confirms the advantages of the proposed framework.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 68 references that are in the map. First posted on arXiv 13 May 2025.

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13 of 68 references.

References · 13

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  5. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  6. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  7. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  8. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  9. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 708
  10. 2019Open-ended Learning in Symmetric Zero-sum Gamescited by 96
  11. 2023Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?cited by 18
  12. 2023Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuveringcited by 5
1 more
  1. 2024A survey of air combat behavior modeling using machine learningcited by 3

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