The ARLIN (Assured Reinforcement Learning Model Interrogation) Toolkit is a research library written in Python that provides explainability outputs and vulnerability detection for Deep Reinforcement Learning (DRL) models, specifically designed to increase model assurance and identify potential points of failure within a trained model. ARLIN utilizes matplotlib and networkx to visualize a trained RL model’s decision making process and provide meaningful vulnerability identification and analysis to researchers. The modular library is structured to easily support custom architecture, algorithm, framework, and analytics modifications and provides a well-documented and tested API for XRL research development and model assurance.

Key functionalities of the ARLIN library include:

• Creation of an XRL dataset with user-defined datapoints from a trained policy with the ability to support custom data, algorithms, and model architectures.

• Dimensionality reduction and embedding generation of a trained model’s latent space.

• Unsupervised clustering of policy latent space outputs based on policy transition data and available XRL attributes.

• Analysis and visualization of policy latent space embeddings and clusters.

• Semi-aggregated Markov decision process (SAMDP) generation and policy-specific path analysis.