SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning

ArXi:2605.08391v1 Announce Type: new Cooperative multi-agent reinforcement learning agents that act on partial local observations face a fundamental information bottleneck: the knowledge needed to select jointly optimal actions is scattered across the team, yet each agent must commit to a decision without access to its teammates' observations, intentions, or chosen actions. Existing methods either ignore this bottleneck, compress it into a scalar mixing signal, or route around it with learned communication channels.