In UAV swarm adversarial applications, multi-agent task allocation requires high-level reasoning and accurate decision-making in dynamic environments. Although large language models (LLMs) have shown strong performance in zero-shot reasoning, they cannot generate optimal allocation strategies without environmental objective feedback. To address this problem, we present A2C-LLM, an Actor-Critic-enhanced large language model for adversarial UAV swarm task allocation. Unlike traditional methods that adopt sequential tracking, we adopt a single-step decision process for macro allocation to improve the efficiency of immediate allocation. In A2C-LLM, the LLM serves as the Actor network to understand the adversarial environment and generate coordination strategies, while a lightweight neural network serves as the Critic network to estimate expected rewards and calculate TD advantage for fine-tuning. Experimental results demonstrate that A2C-LLM significantly outperforms traditional heuristic algorithms and pure LLM baselines in task completion rate and robustness across various adversarial scenarios, showcasing the potential of integrating reinforcement learning feedback with foundation models for autonomous aerial systems.
Bao et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: