Can decentralized agents learn to communicate and coordinate?
Decentralized Collective World Model for Emergent Communication and Coordination
April 7, 2025
https://arxiv.org/pdf/2504.03353This paper proposes a decentralized approach for multi-agent systems to develop a shared language and coordinate actions in dynamic, partially observable environments. It uses world models integrated with communication channels, enabling agents to predict, share information, and learn coordinated behaviors via message exchange. The system employs contrastive learning to align messages between agents, fostering a common symbolic language without centralized control.
Key points for LLM-based multi-agent systems:
- Decentralized Communication: Agents develop a shared symbolic language through bidirectional message exchange, crucial for LLMs to interact and coordinate.
- World Models: Agents use world models to predict and understand their environment, analogous to how LLMs use internal representations to understand text.
- Contrastive Learning: Aligns messages for consistent interpretation across agents, essential for LLMs to understand each other's generated text.
- Partial Observability: Addresses scenarios where agents have limited information, mirroring real-world LLM applications where complete knowledge is unavailable.
- Emergent Communication: The system organically develops a shared language, opening possibilities for LLMs to develop novel communication protocols.
- Coordination in Dynamic Environments: Focuses on coordination in changing situations, directly applicable to dynamic LLM-based interactions.