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MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library
[article]
2020
arXiv
pre-print
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
arXiv:2004.07928v1
fatcat:hb25irjbyfcrdfjzlkb36f46ju