World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks
This work addresses the problem of enhancing autonomous decision-making in low-altitude wireless networks for applications like UAVs, representing an incremental advancement by tailoring existing world model concepts to a specific domain.
The paper tackles the challenge of optimizing wireless edge intelligence by proposing Wireless Dreamer, a world model-based reinforcement learning framework, and demonstrates its effectiveness in improving learning efficiency and decision quality through a weather-aware UAV trajectory planning case study.
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.