Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty

• URL: http://arxiv.org/abs/2602.20729v1
• Date: Tue, 24 Feb 2026 09:50:17 GMT
• Title: Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty
• Authors: Xu Wan, Chao Yang, Cheng Yang, Jie Song, Mingyang Sun,
• Abstract summary: Fuz-RL is a fuzzy measure-guided robust framework for safe RL.<n>We show that Fuz-RL effectively integrates with existing safe RL baselines in a model-free manner.
• Score: 22.020160934935493
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Safe Reinforcement Learning (RL) is crucial for achieving high performance while ensuring safety in real-world applications. However, the complex interplay of multiple uncertainty sources in real environments poses significant challenges for interpretable risk assessment and robust decision-making. To address these challenges, we propose Fuz-RL, a fuzzy measure-guided robust framework for safe RL. Specifically, our framework develops a novel fuzzy Bellman operator for estimating robust value functions using Choquet integrals. Theoretically, we prove that solving the Fuz-RL problem (in Constrained Markov Decision Process (CMDP) form) is equivalent to solving distributionally robust safe RL problems (in robust CMDP form), effectively avoiding min-max optimization. Empirical analyses on safe-control-gym and safety-gymnasium scenarios demonstrate that Fuz-RL effectively integrates with existing safe RL baselines in a model-free manner, significantly improving both safety and control performance under various types of uncertainties in observation, action, and dynamics.

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