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Robust coordination skills enable agents to operate cohesively in sharedenvironments, together towards a common goal and, ideally, individually withouthindering each other's progress. To this end, this paper presents CoordinatedQMIX (CoMIX), a novel training framework for decentralized agents that enablesemergent coordination through flexible policies, allowing at the same timeindependent decision-making at individual level. CoMIX models selfish andcollaborative behavior as incremental steps in each agent's decision process.This allows agents to dynamically adapt their behavior to different situationsbalancing independence and collaboration. Experiments using a variety ofsimulation environments demonstrate that CoMIX outperforms baselines oncollaborative tasks. The results validate our incremental policy approach aseffective technique for improving coordination in multi-agent systems.

CoMIX: A Multi-agent Reinforcement Learning Training Architecture for Efficient Decentralized Coordination and Independent Decision Making