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

Source: http://arxiv.org/abs/2604.13472v1

Summary:

The provided text introduces the Consensus Multi-Agent Transformer (CMAT), a novel framework designed to improve cooperative multi-agent reinforcement learning (MARL) by reformulating it as a hierarchical single-agent problem. While traditional models often struggle with action-generation order sensitivity and unstable training, CMAT utilizes a Transformer-based decoder to iteratively generate a latent consensus vector. This shared strategy allows all agents to select their actions simultaneously and independently while remaining highly coordinated. By treating the collective of agents as a unified entity, the system can be optimized using standard Proximal Policy Optimization (PPO). Extensive testing across benchmarks like StarCraft II and Google Research Football demonstrates that this consensus-driven approach consistently outperforms existing centralized and sequential baselines. Ultimately, the research offers a more robust method for reaching optimal joint decisions in complex, multi-agent environments.