In multi-agent systems, agents possess only local observations of the environment. Communication between teammates becomes crucial for enhancing coordination. Past research has primarily focused on encoding local information into embedding messages which are unintelligible to humans. We find that using these messages in agent's policy learning leads to brittle policies when tested on out-of-distribution initial states. We present an approach to multi-agent coordination, where each agent is equipped with the capability to integrate its (history of) observations, actions and messages received into a Common Operating Picture (COP) and disseminate the COP. This process takes into account the dynamic nature of the environment and the shared mission. We conducted experiments in the StarCraft2 environment to validate our approach. Our results demonstrate the efficacy of COP integration, and show that COP-based training leads to robust policies compared to state-of-the-art Multi-Agent Reinforcement Learning (MARL) methods when faced with out-of-distribution initial states.

多智能体系统中，代理人之间的通信对于加强协调至关重要。本研究提出了一种多智能体协调的方法，其中每个代理人具备将其观察、行动和接收到的信息整合到一个公共操作图中并传播的能力。实验结果表明，与最先进的多智能体强化学习方法相比，基于公共操作图的训练方法在面对分布不均的初始状态时能够产生出鲁棒的策略。

通过公共操作图整合增强多智能体协作能力