mlsanityData to 14 Oct 2026

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

arXiv 2506.17297 ↗First posted 17 Jun 2025cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → Safe and constrained RL

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 7 references that are in the map. First posted on arXiv 17 Jun 2025.

Builds on

3 of 7 references.

References · 3

  1. 2017Constrained Policy Optimizationcited by 983
  2. 2019Lyapunov-based Safe Policy Optimization for Continuous Controlcited by 145
  3. 2017Visualizing and Understanding Atari Agentscited by 141

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  6. 2025SB-TRPO: Towards Safe Reinforcement Learning with Hard Constraints
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  8. 2024Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints