用世界模型提升智能体在边缘网络中的自主决策能力
World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks
- 构建环境内部表征，实现高效预测与规划
- 在低空无线网络中显著提升无人机路径规划效率
- 适合研究智能边缘计算与自主系统的人参考
世界模型正成为人工智能的变革性范式，使智能体能够构建对环境的内部表征，以实现预测推理、规划和决策。通过学习潜在动态，世界模型提供了一种样本高效的框架，在数据受限或安全敏感场景中尤为关键。本文全面综述了世界模型的架构、训练范式及其在预测、生成、规划和因果推理中的应用。我们对比并区分了世界模型与数字孪生、元宇宙及基础模型的关系，明确了其作为自主智能体嵌入式认知引擎的独特作用。进一步提出无线梦境者（Wireless Dreamer），一种面向无线边缘智能优化的世界模型强化学习框架，特别适用于低空无线网络（LAWN）。通过气象感知的无人机轨迹规划案例研究，验证了该框架在提升学习效率与决策质量方面的有效性。
原文摘要 · Abstract (English)
World models are emerging as a transformative paradigm in artificial intelligence, enabling agents to construct internal representations of their environments for predictive reasoning, planning, and decision-making. By learning latent dynamics, world models provide a sample-efficient framework that is especially valuable in data-constrained or safety-critical scenarios. In this paper, we present a comprehensive overview of world models, highlighting their architecture, training paradigms, and applications across prediction, generation, planning, and causal reasoning. We compare and distinguish world models from related concepts such as digital twins, the metaverse, and foundation models, clarifying their unique role as embedded cognitive engines for autonomous agents. We further propose Wireless Dreamer, a novel world model-based reinforcement learning framework tailored for wireless edge intelligence optimization, particularly in low-altitude wireless networks (LAWNs). Through a weather-aware UAV trajectory planning case study, we demonstrate the effectiveness of our framework in improving learning efficiency and decision quality.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。