  In cooperative Multi-Agent Reinforcement Learning (MARL) agents are required
to learn behaviours as a team to achieve a common goal. However, while learning
a task, some agents may end up learning sub-optimal policies, not contributing
to the objective of the team. Such agents are called lazy agents due to their
non-cooperative behaviours that may arise from failing to understand whether
they caused the rewards. As a consequence, we observe that the emergence of
cooperative behaviours is not necessarily a byproduct of being able to solve a
task as a team. In this paper, we investigate the applications of causality in
MARL and how it can be applied in MARL to penalise these lazy agents. We
observe that causality estimations can be used to improve the credit assignment
to the agents and show how it can be leveraged to improve independent learning
in MARL. Furthermore, we investigate how Amortized Causal Discovery can be used
to automate causality detection within MARL environments. The results
demonstrate that causality relations between individual observations and the
team reward can be used to detect and punish lazy agents, making them develop
more intelligent behaviours. This results in improvements not only in the
overall performances of the team but also in their individual capabilities. In
addition, results show that Amortized Causal Discovery can be used efficiently
to find causal relations in MARL.
