去中心化多智能体模型实现通信与协作同步涌现。
Decentralized Collective World Model for Emergent Communication and Coordination
- 通过扩展集体预测编码，让智能体自主生成符号并协同行动。
- 在轨迹绘制任务中，性能仅次于集中式模型，且在感知差异下表现更优。
- 限制直接访问他人状态，促使更贴近环境的符号系统自然形成。
我们提出一种完全去中心化的多智能体世界模型，通过时间扩展的集体预测编码，同时实现通信符号的涌现与协调行为。不同于以往分别研究通信或协作的研究，本方法二者兼顾。模型将世界模型与通信通道融合，使智能体能预测环境动态、从部分观测中推断状态，并通过双向消息交换与对比学习对齐信息。在双智能体轨迹绘制任务中，该通信方法在感知能力差异情况下优于非通信模型，协调性能仅次于集中式模型。更重要的是，受限制的去中心化架构（禁止直接访问他人内部状态）促进了更符合环境状态的有意义符号系统的涌现。结果表明，去中心化通信可有效支持协作，同时发展共享环境表征。
原文摘要 · Abstract (English)
We propose a fully decentralized multi-agent world model that enables both symbol emergence for communication and coordinated behavior through temporal extension of collective predictive coding. Unlike previous research that focuses on either communication or coordination separately, our approach achieves both simultaneously. Our method integrates world models with communication channels, enabling agents to predict environmental dynamics, estimate states from partial observations, and share critical information through bidirectional message exchange with contrastive learning for message alignment. Using a two-agent trajectory drawing task, we demonstrate that our communication-based approach outperforms non-communicative models when agents have divergent perceptual capabilities, achieving the second-best coordination after centralized models. Importantly, our decentralized approach with constraints preventing direct access to other agents' internal states facilitates the emergence of more meaningful symbol systems that accurately reflect environmental states. These findings demonstrate the effectiveness of decentralized communication for supporting coordination while developing shared representations of the environment.
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