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.

我们提出了一种新的多智能体强化学习算法，将大型语言模型嵌入到智能体中，使其能够生成人类可理解的口头沟通，通过在智能体之间进行信息共享来显著提高学习效率和性能，并为人类提供解释多智能体合作过程的工具。

Verco：多智能体强化学习中协调语言沟通的学习

Verco: Learning Coordinated Verbal Communication for Multi-agent  Reinforcement Learning

In this paper, we propose leveraging causal generative learning as an
interpretable tool for explaining image classifiers. Specifically, we present a
generative counterfactual inference approach to study the influence of visual
features (i.e., pixels) as well as causal factors through generative learning.
To this end, we first uncover the most influential pixels on a classifier's
decision by varying the value of a causal attribute via counterfactual
inference and computing both Shapely and contrastive explanations for
counterfactual images with these different attribute values. We then establish
a Monte-Carlo mechanism using the generator of a causal generative model in
order to adapt Shapley explainers to produce feature importances for the
human-interpretable attributes of a causal dataset in the case where a
classifier has been trained exclusively on the images of the dataset. Finally,
we present optimization methods for creating counterfactual explanations of
classifiers by means of counterfactual inference, proposing straightforward
approaches for both differentiable and arbitrary classifiers. We exploit the
Morpho-MNIST causal dataset as a case study for exploring our proposed methods
for generating counterfacutl explantions. We employ visual explanation methods
from OmnixAI open source toolkit to compare them with our proposed methods. By
employing quantitative metrics to measure the interpretability of
counterfactual explanations, we find that our proposed methods of
counterfactual explanation offer more interpretable explanations compared to
those generated from OmnixAI. This finding suggests that our methods are
well-suited for generating highly interpretable counterfactual explanations on
causal datasets.

提出使用因果生成学习作为解释图像分类器的可解释工具，利用生成对事实推理方法研究视觉特征和因果因素对分类器决策的影响，提供了针对可解释因果数据集的对抗解释方法，通过与 OmnixAI 开源工具进行对比，发现我们的方法提供的对事实解释更可解释，适用于生成高度可解释的对事实解释。