mlsanityData to 14 Oct 2026

A Learning Framework For Cooperative Collision Avoidance of UAV Swarms Leveraging Domain Knowledge

arXiv 2507.10913 ↗First posted 15 Jul 2025cs.MA cs.LG cs.ROcited by 0

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

Abstract (arXiv)

This paper presents a multi-agent reinforcement learning (MARL) framework for cooperative collision avoidance of UAV swarms leveraging domain knowledge-driven reward. The reward is derived from knowledge in the domain of image processing, approximating contours on a two-dimensional field. By modeling obstacles as maxima on the field, collisions are inherently avoided as contours never go through peaks or intersect. Additionally, counters are smooth and energy-efficient. Our framework enables training with large swarm sizes as the agent interaction is minimized and the need for complex credit assignment schemes or observation sharing mechanisms in state-of-the-art MARL approaches are eliminated. Moreover, UAVs obtain the ability to adapt to complex environments where contours may be non-viable or non-existent through intensive training. Extensive experiments are conducted to evaluate the performances of our framework against state-of-the-art MARL algorithms.

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 26 references that are in the map. First posted on arXiv 15 Jul 2025.

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10 of 26 references.

References · 10

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  3. 2017Counterfactual Multi-Agent Policy Gradientscited by 751
  4. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 708
  5. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
  6. 2020Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorizationcited by 26
  7. 2024Multi-Agent Imitation Learning: Value is Easy, Regret is Hardcited by 11
  8. 2023E2CoPre: Energy Efficient and Cooperative Collision Avoidance for UAV Swarms with Trajectory Predictioncited by 2
  9. 2021E2Coop: Energy Efficient and Cooperative Obstacle Detection and Avoidance for UAV Swarmscited by 1
  10. 2022CoDe: A Cooperative and Decentralized Collision Avoidance Algorithm for Small-Scale UAV Swarms Considering Energy Efficiencycited by 1

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  5. 2025Dynamic one-time delivery of critical data by small and sparse UAV swarms: a model problem for MARL scaling studies
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  8. 2025Decentralized Consensus Inference-based Hierarchical Reinforcement Learning for Multi-Constrained UAV Pursuit-Evasion Game