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April 1, 2026Applied Soft ComputingOpen Access

Efficient multi-agent deep reinforcement learning algorithm for multi UAV collision avoidance

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This research aims to develop an efficient multi-agent deep reinforcement learning algorithm for UAV collision avoidance.
Implemented a Multi-Agent Deep Reinforcement Learning Algorithm using a graph attention network.
Applied curriculum learning and transfer learning to enhance scalability and coordination.
Analyzed performance in dense multi-agent drone environments.
Achieved 17% higher cumulative reward compared to the benchmark.
Reduced loss-of-separation time steps by up to 10%.
Limited active interaction edges by approximately 44% versus existing methods.
Improved decision-making stability by reducing action-selection bias.

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Multi-agent reinforcement learning improves collision avoidance for UAVs, indicating enhanced safety in congested airspace.

Rezaee et al. (2026) studied this question.

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