论文标题
Marleme：一个多机构增强学习模型提取库
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library
论文作者
论文摘要
多机构增强学习（MARL）涵盖了在各种领域应用的强大方法。进一步增强这些方法论的一种有效方法是开发可以扩展其可解释性和解释性的库和工具。在这项工作中，我们介绍了Marleme：MARL模型提取库，旨在通过使用符号模型近似MARL系统来提高MARL系统的解释性。符号模型具有高度的可解释性，定义明确的特性和可验证的行为。因此，它们可用于检查和更好地理解基本的MARL系统和相应的MARL代理，并替代所有特别安全和保障的所有/某些代理。
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.
