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

Reinforcement Learning (RL) has achieved remarkable success across a wide range of domains, from game playing to robotic control and autonomous decision-making. However, the deployment of RL agents in real-world safety-critical applications remains a significant challenge due to two key limitations: (1) the lack of safety guarantees during exploration and policy execution, and (2) the opaqueness of learned policies, which hinders human understanding and trust. In practical domains such as autonomous driving, industrial automation, and clinical decision support, agents are often required to operate under hard constraints: for example, to avoid collisions, respect velocity limits, or obey medical safety protocols. Standard RL algorithms, such as Deep Q-Networks (DQN), are typically designed to maximize cumulative reward without any explicit notion of constraint satisfaction. Violations of such constraints can lead to catastrophic outcomes, rendering these agents unusable in safety-sensitive contexts.