Efficient resource allocation for unmanned aerial vehicle (UAV) swarms is a critical challenge, complicated by severe interference between UAV-to-UAV (U2U) and UAV-to-infrastructure (U2I) communications. Traditional Multi-Agent Reinforcement Learning (MARL) methods often prove insufficient in this domain due to two fundamental limitations: the policy sacrifice phenomenon, wherein uncoordinated agent competition leads to suboptimal outcomes, and the curse of dimensionality, which impedes effective learning in large swarms. To address these limitations, this paper proposes the Attention-based and Dynamic Gateway Multi-Agent Soft Actor-Critic (ADG-MASAC), a novel MARL framework. Our approach integrates a dynamic gateway mechanism to convert chaotic competition into structured collaboration via dynamic role assignment and an attention-based critic to enable precise perception of high-dimensional global states. Experimental results demonstrate that ADG-MASAC not only resolves the policy sacrifice issue but also achieves substantial performance gains in both U2U and U2I communications. Ablation studies further confirm that the synergy between these two mechanisms is essential for the algorithm’s success.
Qu et al. (2026) studied this question.