  In recent years, multi-agent reinforcement learning algorithms have made
significant advancements in diverse gaming environments, leading to increased
interest in the broader application of such techniques. To address the
prevalent challenge of partial observability, communication-based algorithms
have improved cooperative performance through the sharing of numerical
embedding between agents. However, the understanding of the formation of
collaborative mechanisms is still very limited, making designing a
human-understandable communication mechanism a valuable problem to address. In
this paper, we propose a novel multi-agent reinforcement learning algorithm
that embeds large language models into agents, endowing them with the ability
to generate human-understandable verbal communication. The entire framework has
a message module and an action module. The message module is responsible for
generating and sending verbal messages to other agents, effectively enhancing
information sharing among agents. To further enhance the message module, we
employ a teacher model to generate message labels from the global view and
update the student model through Supervised Fine-Tuning (SFT). The action
module receives messages from other agents and selects actions based on current
local observations and received messages. Experiments conducted on the
Overcooked game demonstrate our method significantly enhances the learning
efficiency and performance of existing methods, while also providing an
interpretable tool for humans to understand the process of multi-agent
cooperation.
