用大模型指导强化学习，提升非地面网络资源分配性能。
Large Artificial Intelligence Model Guided Deep Reinforcement Learning for Resource Allocation in Non Terrestrial Networks
- 大语言模型生成文本指令，动态调节强化学习奖励函数。
- 极端天气下吞吐量提升64%，公平性和掉线率显著改善。
- 适合研究智能通信系统与大模型融合的科研人员。
大型人工智能模型（LAM）已被应用于非地面网络（NTN），凭借其强大的泛化能力及减少特定任务训练的优势表现出更优性能。本文提出一种由大型语言模型（LLM）引导的深度强化学习（DRL）代理。该LLM作为高层协调者，生成文本指导以在训练过程中调整DRL代理的奖励函数。结果表明，在正常天气条件下，LAM-DRL相比传统DRL性能提升40%；在极端天气条件下，相比启发式方法，吞吐量、公平性和掉线概率均有显著改善，性能提升达64%。
原文摘要 · Abstract (English)
Large AI Model (LAM) have been proposed to applications of Non-Terrestrial Networks (NTN), that offer better performance with its great generalization and reduced task specific trainings. In this paper, we propose a Deep Reinforcement Learning (DRL) agent that is guided by a Large Language Model (LLM). The LLM operates as a high level coordinator that generates textual guidance that shape the reward of the DRL agent during training. The results show that the LAM-DRL outperforms the traditional DRL by 40% in nominal weather scenarios and 64% in extreme weather scenarios compared to heuristics in terms of throughput, fairness, and outage probability.
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