mlsanityData to 14 Oct 2026

AC-MASAC: An Attentive Curriculum Learning Framework for Heterogeneous UAV Swarm Coordination

arXiv 2602.11735 ↗First posted 12 Feb 2026cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Cooperative path planning for heterogeneous UAV swarms poses significant challenges for Multi-Agent Reinforcement Learning (MARL), particularly in handling asymmetric inter-agent dependencies and addressing the risks of sparse rewards and catastrophic forgetting during training. To address these issues, this paper proposes an attentive curriculum learning framework (AC-MASAC). The framework introduces a role-aware heterogeneous attention mechanism to explicitly model asymmetric dependencies. Moreover, a structured curriculum strategy is designed, integrating hierarchical knowledge transfer and stage-proportional experience replay to address the issues of sparse rewards and catastrophic forgetting. The proposed framework is validated on a custom multi-agent simulation platform, and the results show that our method has significant advantages over other advanced methods in terms of Success Rate, Formation Keeping Rate, and Success-weighted Mission Time. The code is available at textcolorredhttps://github.com/Wanhao-Liu/AC-MASAC.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 24 references that are in the map. First posted on arXiv 12 Feb 2026.

Builds on

10 of 24 references.

References · 10

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  3. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
  4. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  5. 2019QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learningcited by 379
  6. 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 261
  7. 2015Multiagent Cooperation and Competition with Deep Reinforcement Learningcited by 184
  8. 2019Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Criticscited by 47
  9. 2020Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computingcited by 24
  10. 2024T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integrationcited by 10

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