Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus
Cooperative multi-agent reinforcement learning (MARL) is widely used to address large joint observation and action spaces by decomposing a centralized control problem into multiple interacting agents. However, such decomposition often introduces additional challenges, including non-stationarity, unstable training, weak coordination, and limited theoretical guarantees. In this paper, we propose the Consensus Multi-Agent Transformer (CMAT), a centralized framework that bridges cooperative MARL to a hierarchical single-agent reinforcement learning (SARL) formulation. CMAT treats all agents as a unified entity and employs a Transformer encoder to process the large joint observation space. To ...
| Authors | Zijian Zhao, Jing Gao, Sen Li |
|---|---|
| Primary category | cs.LG |
| Published | 15 April 2026 |
| Last updated at source | 27 July 2026 |
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