SCALE-COMM: Shared, Contrastively-Aligned Latent Embeddings for MARL Communication
SCALE-COMM is a self-supervised framework that decouples communication learning from policy optimization, learning compact, stable, and policy-relevant latent messages to improve coordination in multi-agent reinforcement learning. It outperforms existing methods on benchmarks and a realistic warehouse task, offering better stability, sample efficiency, and throughput.
Article intelligence
Key points
- Decouples communication learning from policy optimization to reduce interference.
- Uses contrastive learning to enforce consistency across agents and time.
- Outperforms existing frameworks in both standard and realistic coordination tasks.
Why it matters
This matters because decouples communication learning from policy optimization to reduce interference.
Technical impact
May affect agent architecture, tool calling, workflow automation, and product integration.
[2605.27532] SCALE-COMM: Shared, Contrastively-Aligned Latent Embeddings for MARL Communication
[Submitted on 26 May 2026]
Title:SCALE-COMM: Shared, Contrastively-Aligned Latent Embeddings for MARL Communication
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Abstract:Emergent communication enables partially observant Autonomous Mobile Robots (AMRs) to coordinate effectively in decentralized multi-agent reinforcement learning (MARL) settings. However, existing approaches often struggle with unstable communication protocols, ungrounded message semantics, and interference between communication learning and policy optimization, leading to degraded coordination over time. We propose SCALE-COMM (Shared, Contrastively-Aligned Latent Embeddings for COMMunication), a self-supervised framework for learning compact, stable, and policy-relevant communication representations. SCALE-COMM decouples communication learning from policy optimization by training low-dimensional latent messages that capture task-relevant planning and traffic information, while enforcing consistency across agents and time. Across standard MARL benchmarks and a realistic warehouse coordination task, SCALE-COMM consistently outperforms existing communication frameworks in both representation quality and task performance. The learned communication space yields improved stability, sample efficiency, and throughput under policy fine-tuning, demonstrating the effectiveness of representation-driven communication for scalable multi-agent coordination.
Comments: IEEE IV 2026
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.27532 [cs.RO]
(or arXiv:2605.27532v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.27532
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mahmoud Abouelyazid [view email] [v1] Tue, 26 May 2026 18:06:19 UTC (5,898 KB)
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