World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks

TL;DR

This paper reviews world models as a key AI paradigm for autonomous agents, introduces Wireless Dreamer for edge network optimization, and demonstrates its effectiveness through UAV trajectory planning.

Contribution

It provides a comprehensive overview of world models and introduces Wireless Dreamer, a novel reinforcement learning framework for wireless edge intelligence.

Findings

Wireless Dreamer improves learning efficiency in UAV planning.

The framework enhances decision quality in low-altitude wireless networks.

Case study validates effectiveness in weather-aware UAV trajectory planning.

Abstract

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.