  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.

Machine Learning;Artificial Intelligence;Multiagent Systems