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arXiv · 2506.17297

SafeRL-Lite: A Lightweight, Explainable, and Constrained Reinforcement Learning Library

Satyam Mishra·Phung Thao Vi·Shivam Mishra·Vishwanath Bijalwan·Vijay Bhaskar Semwal·Abdul Manan Khan
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Abstract

We introduce SafeRL-Lite, an open-source Python library for building reinforcement learning (RL) agents that are both constrained and explainable. Existing RL toolkits often lack native mechanisms for enforcing hard safety constraints or producing human-interpretable rationales for decisions. SafeRL-Lite provides modular wrappers around standard Gym environments and deep Q-learning agents to enable: (i) safety-aware training via constraint enforcement, and (ii) real-time post-hoc explanation via SHAP values and saliency maps. The library is lightweight, extensible, and installable via pip, and includes built-in metrics for constraint violations. We demonstrate its effectiveness on constrained variants of CartPole and provide visualizations that reveal both policy logic and safety adherence. The full codebase is available at: https://github.com/satyamcser/saferl-lite.

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Satyam Mishra, Phung Thao Vi, Shivam Mishra, Vishwanath Bijalwan, Vijay Bhaskar Semwal, Abdul Manan Khan. 2025-06-17. SafeRL-Lite: A Lightweight, Explainable, and Constrained Reinforcement Learning Library. https://arxiv.org/abs/2506.17297

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