SafeRL-Lite: A Lightweight, Explainable, and Constrained Reinforcement Learning Library
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Mishra, Satyam Vi, Phung Thao Mishra, Shivam Bijalwan, Vishwanath Semwal, Vijay Bhaskar Khan, Abdul Manan |
| author_facet | Mishra, Satyam Vi, Phung Thao Mishra, Shivam Bijalwan, Vishwanath Semwal, Vijay Bhaskar Khan, Abdul Manan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17297 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SafeRL-Lite: A Lightweight, Explainable, and Constrained Reinforcement Learning Library Mishra, Satyam Vi, Phung Thao Mishra, Shivam Bijalwan, Vishwanath Semwal, Vijay Bhaskar Khan, Abdul Manan Machine Learning Artificial Intelligence 68T05 I.2.6; I.2.8 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. |
| title | SafeRL-Lite: A Lightweight, Explainable, and Constrained Reinforcement Learning Library |
| topic | Machine Learning Artificial Intelligence 68T05 I.2.6; I.2.8 |
| url | https://arxiv.org/abs/2506.17297 |