MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library

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 approaches 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 systems and corresponding MARL agents, as well as to replace all/some of the agents that are particularly safety and security critical. In this work, we demonstrate how MARLeME can be applied to two well-known case studies (Cooperative Navigation and RoboCup Takeaway), using extracted models based on Abstract Argumentation.