  Multi-Agent Reinforcement Learning (MARL) encompasses a powerful class of
methodologies that have been applied in a wide range of fields. An effective
way to further empower these methodologies is to develop libraries and tools
that could expand their interpretability and explainability. In this work, we
introduce MARLeME: a MARL model extraction library, designed to improve
explainability of MARL systems by approximating them with symbolic models.
Symbolic models offer a high degree of interpretability, well-defined
properties, and verifiable behaviour. Consequently, they can be used to inspect
and better understand the underlying MARL system and corresponding MARL agents,
as well as to replace all/some of the agents that are particularly safety and
security critical.
