CoMIX: A Multi-agent Reinforcement Learning Training Architecture for
  Efficient Decentralized Coordination and Independent Decision Making
Giovanni Minelli Mirco Musolesi
Robust coordination skills enable agents to operate cohesively in shared
environments, together towards a common goal and, ideally, individually without
hindering each other's progress. To this end, this paper presents Coordinated
QMIX (CoMIX), a novel training framework for decentralized agents that enables
emergent coordination through flexible policies, allowing at the same time
independent decision-making at individual level. CoMIX models selfish and
collaborative behavior as incremental steps in each agent's decision process.
This allows agents to dynamically adapt their behavior to different situations
balancing independence and collaboration. Experiments using a variety of
simulation environments demonstrate that CoMIX outperforms baselines on
collaborative tasks. The results validate our incremental policy approach as
effective technique for improving coordination in multi-agent systems.