  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.
