Collaboration is a necessary skill to perform tasks that are beyond one
agent's capabilities. Addressed extensively in both conventional and modern AI,
multi-agent collaboration has often been studied in the context of simple grid
worlds. We argue that there are inherently visual aspects to collaboration
which should be studied in visually rich environments. A key element in
collaboration is communication that can be either explicit, through messages,
or implicit, through perception of the other agents and the visual world.
Learning to collaborate in a visual environment entails learning (1) to perform
the task, (2) when and what to communicate, and (3) how to act based on these
communications and the perception of the visual world. In this paper we study
the problem of learning to collaborate directly from pixels in AI2-THOR and
demonstrate the benefits of explicit and implicit modes of communication to
perform visual tasks. Refer to our project page for more details:
this https URL

本研究探讨了多智能体协作在视觉丰富的环境中的视觉学习问题，实现了在 AI2-THOR 中从像素级开始学习协作任务，并展示了显式和隐式通信对于实现视觉任务的好处。