Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The Consensus Multi-Agent Transformer (CMAT) is a novel centralized framework designed to bridge cooperative Multi-Agent Reinforcement Learning (MARL) with hierarchical Single-Agent Reinforcement Learning (SARL). CMAT addresses challenges in MARL, such as non-stationarity, unstable training, and order-dependent action generation in conventional Multi-Agent Transformers (MAT). It employs a Transformer encoder to process joint observations and a Transformer decoder to autoregressively generate a high-level "consensus vector," simulating agents agreeing on strategies in a latent space. This consensus then conditions all agents to generate actions simultaneously, ensuring order-independent joint decision-making. The joint policy is optimized using single-agent Proximal Policy Optimization (PPO). Experiments on StarCraft II, Multi-Agent MuJoCo, and Google Research Football benchmarks demonstrate CMAT's superior performance over existing centralized and sequential MARL solutions, with further enhancements observed through fine-tuning.

Key takeaway

For research scientists developing multi-agent reinforcement learning systems, CMAT offers a robust approach to overcome the limitations of order-dependent action generation and unstable training inherent in conventional MAT. You should consider implementing CMAT to achieve superior coordination and global optimization in fully observable cooperative tasks, especially where simultaneous decision-making is critical. Its SARL-based optimization provides stronger theoretical guarantees for convergence towards optimal solutions.

Key insights

CMAT uses a latent consensus mechanism to enable order-independent, simultaneous multi-agent actions within a SARL framework.

Principles

• Order-independent action generation improves multi-agent coordination.
• Hierarchical SARL can mitigate MARL training instability.
• Latent consensus vectors facilitate expressive coordination.

Method

CMAT processes joint observations via a Transformer encoder, generates a consensus vector iteratively with a Transformer decoder, and then simultaneously produces agent actions conditioned on this consensus, optimizing with single-agent PPO.

In practice

• Apply CMAT for fully observable cooperative MARL tasks.
• Consider fine-tuning CMAT for performance enhancement.
• Use consensus iteration count equal to agent number.

Topics

• Consensus Multi-Agent Transformer
• Multi-Agent Reinforcement Learning
• Single-Agent Reinforcement Learning
• Transformer Architecture
• Order-Independent Decision Making

Code references

• RS2002/CMAT
• PKU-MARL/Multi-Agent-Transformer

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Architect

Related on AIssential

• Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus — CMAT bridges MARL to SARL using a Transformer and latent consensus for order-independent joint action.
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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.