Multi-Agent Reinforcement Learning (MARL) comprises an area of growing
interest in the field of machine learning. Despite notable advances, there are
still problems that require investigation. The lazy agent pathology is a famous
problem in MARL that denotes the event when some of the agents in a MARL team
do not contribute to the common goal, letting the teammates do all the work. In
this work, we aim to investigate this problem from a causality-based
perspective. We intend to create the bridge between the fields of MARL and
causality and argue about the usefulness of this link. We study a fully
decentralised MARL setup where agents need to learn cooperation strategies and
show that there is a causal relation between individual observations and the
team reward. The experiments carried show how this relation can be used to
improve independent agents in MARL, resulting not only on better performances
as a team but also on the rise of more intelligent behaviours on individual
agents.

通过从因果关系的角度研究，本文探讨了多智能体强化学习中的懒惰代理问题，并将其与因果关系领域建立联系，通过实验证明个体观察与团队奖励之间存在因果关系，进一步提高多智能体强化学习中独立代理的性能和智能行为。

在多智能体环境中独立地从因果关系中学习

Learning Independently from Causality in Multi-Agent Environments

Context-Aware Emotion Recognition (CAER) is a crucial and challenging task
that aims to perceive the emotional states of the target person with contextual
information. Recent approaches invariably focus on designing sophisticated
architectures or mechanisms to extract seemingly meaningful representations
from subjects and contexts. However, a long-overlooked issue is that a context
bias in existing datasets leads to a significantly unbalanced distribution of
emotional states among different context scenarios. Concretely, the harmful
bias is a confounder that misleads existing models to learn spurious
correlations based on conventional likelihood estimation, significantly
limiting the models' performance. To tackle the issue, this paper provides a
causality-based perspective to disentangle the models from the impact of such
bias, and formulate the causalities among variables in the CAER task via a
tailored causal graph. Then, we propose a Contextual Causal Intervention Module
(CCIM) based on the backdoor adjustment to de-confound the confounder and
exploit the true causal effect for model training. CCIM is plug-in and
model-agnostic, which improves diverse state-of-the-art approaches by
considerable margins. Extensive experiments on three benchmark datasets
demonstrate the effectiveness of our CCIM and the significance of causal
insight.

本文提供了基于因果关系的视角来解决 “上下文感知情感识别（CAER）” 中的数据集中存在的偏见问题。通过构建因果图，设计了 Contextual Causal Intervention Module (CCIM) 来提高模型表现并且在三个基准数据集上进行了充分的实验来证明其有效性和重要性。