用大模型增强强化学习，解决低空经济网络的复杂决策难题。
Large Language Model-enhanced Reinforcement Learning for Low-Altitude Economy Networking
- 用大模型处理信息、设计奖励、做决策、生成策略
- 通过大模型设计奖励函数，提升强化学习在低空网络中的表现
- 适合关注低空智能网络与大模型融合的科研与工程人员
低空经济网络（LAENet）旨在通过部署各类飞行器，在1000米以下实现灵活且低成本的空中组网，以支持多样化的飞行应用。然而，复杂的决策需求、资源受限以及环境不确定性给其发展带来挑战。强化学习（RL）虽可应对这些问题，但存在泛化能力差、奖励设计困难和模型不稳定等局限。大语言模型（LLM）的出现为克服这些瓶颈提供了新机遇。本文首先介绍如何利用大模型的生成、上下文理解与结构化推理能力，将大模型融入强化学习框架。随后，提出一种面向低空经济网络的LLM增强型强化学习框架，将大模型作为信息处理、奖励设计、决策执行和策略生成的核心组件。进一步通过案例研究，使用大模型设计奖励函数，显著提升了强化学习在低空网络中的学习性能。最后总结研究成果，并展望未来方向。
原文摘要 · Abstract (English)
Low-Altitude Economic Networking (LAENet) aims to support diverse flying applications below 1,000 meters by deploying various aerial vehicles for flexible and cost-effective aerial networking. However, complex decision-making, resource constraints, and environmental uncertainty pose significant challenges to the development of the LAENet. Reinforcement learning (RL) offers a potential solution in response to these challenges but has limitations in generalization, reward design, and model stability. The emergence of large language models (LLMs) offers new opportunities for RL to mitigate these limitations. In this paper, we first present a tutorial about integrating LLMs into RL by using the capacities of generation, contextual understanding, and structured reasoning of LLMs. We then propose an LLM-enhanced RL framework for the LAENet in terms of serving the LLM as information processor, reward designer, decision-maker, and generator. Moreover, we conduct a case study by using LLMs to design a reward function to improve the learning performance of RL in the LAENet. Finally, we provide a conclusion and discuss future work.
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