Communicating Plans, Not Percepts: Scalable Multi-Agent Coordination with Embodied World Models

• URL: http://arxiv.org/abs/2508.02912v3
• Date: Tue, 04 Nov 2025 06:42:28 GMT
• Title: Communicating Plans, Not Percepts: Scalable Multi-Agent Coordination with Embodied World Models
• Authors: Brennen A. Hill, Mant Koh En Wei, Thangavel Jishnuanandh,
• Abstract summary: A central question in Multi-Agent Reinforcement Learning (MARL) is whether to engineer communication protocols or learn them end-to-end.<n>We propose and compare two communication strategies for a cooperative task-allocation problem.<n>Our experiments reveal that while emergent communication is viable in simple settings, the engineered, world model-based approach shows superior performance, sample efficiency, and scalability as complexity increases.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robust coordination is critical for effective decision-making in multi-agent systems, especially under partial observability. A central question in Multi-Agent Reinforcement Learning (MARL) is whether to engineer communication protocols or learn them end-to-end. We investigate this dichotomy using embodied world models. We propose and compare two communication strategies for a cooperative task-allocation problem. The first, Learned Direct Communication (LDC), learns a protocol end-to-end. The second, Intention Communication, uses an engineered inductive bias: a compact, learned world model, the Imagined Trajectory Generation Module (ITGM), which uses the agent's own policy to simulate future states. A Message Generation Network (MGN) then compresses this plan into a message. We evaluate these approaches on goal-directed interaction in a grid world, a canonical abstraction for embodied AI problems, while scaling environmental complexity. Our experiments reveal that while emergent communication is viable in simple settings, the engineered, world model-based approach shows superior performance, sample efficiency, and scalability as complexity increases. These findings advocate for integrating structured, predictive models into MARL agents to enable active, goal-driven coordination.

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