MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library

Understand

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.