  In this work, we address challenging multi-agent cooperation problems with
decentralized control, raw sensory observations, costly communication, and
multi-objective tasks instantiated in various embodied environments. While
previous research either presupposes a cost-free communication channel or
relies on a centralized controller with shared observations, we harness the
commonsense knowledge, reasoning ability, language comprehension, and text
generation prowess of LLMs and seamlessly incorporate them into a
cognitive-inspired modular framework that integrates with perception, memory,
and execution. Thus building a Cooperative Embodied Language Agent CoELA, who
can plan, communicate, and cooperate with others to accomplish long-horizon
tasks efficiently. Our experiments on C-WAH and TDW-MAT demonstrate that CoELA
driven by GPT-4 can surpass strong planning-based methods and exhibit emergent
effective communication. Though current Open LMs like LLAMA-2 still
underperform, we fine-tune a CoELA with data collected with our agents and show
how they can achieve promising performance. We also conducted a user study for
human-agent interaction and discovered that CoELA communicating in natural
language can earn more trust and cooperate more effectively with humans. Our
research underscores the potential of LLMs for future research in multi-agent
cooperation. Videos can be found on the project website
https://vis-www.cs.umass.edu/Co-LLM-Agents/.
