  In multi-agent systems, noise reduction techniques are important for
improving the overall system reliability as agents are required to rely on
limited environmental information to develop cooperative and coordinated
behaviors with the surrounding agents. However, previous studies have often
applied centralized noise reduction methods to build robust and versatile
coordination in noisy multi-agent environments, while distributed and
decentralized autonomous agents are more plausible for real-world application.
In this paper, we introduce a \emph{distributed attentional actor architecture
model for a multi-agent system} (DA3-X), using which we demonstrate that agents
with DA3-X can selectively learn the noisy environment and behave
cooperatively. We experimentally evaluate the effectiveness of DA3-X by
comparing learning methods with and without DA3-X and show that agents with
DA3-X can achieve better performance than baseline agents. Furthermore, we
visualize heatmaps of \emph{attentional weights} from the DA3-X to analyze how
the decision-making process and coordinated behavior are influenced by noise.
