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[2602.20729] Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty
Summary
The paper presents Fuz-RL, a fuzzy-guided framework for safe reinforcement learning that addresses uncertainties in real-world applications, enhancing safety and control performance.
Why It Matters
As reinforcement learning is increasingly applied in critical areas, ensuring safety under uncertainty is paramount. Fuz-RL offers a novel approach to risk assessment and decision-making, potentially transforming how RL systems operate in unpredictable environments.
Key Takeaways
- Fuz-RL integrates fuzzy measures for robust decision-making in reinforcement learning.
- The framework improves safety and control performance in uncertain environments.
- Empirical results show Fuz-RL's effectiveness compared to existing safe RL methods.
Computer Science > Machine Learning arXiv:2602.20729 (cs) [Submitted on 24 Feb 2026] 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 View a PDF of the paper titled Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty, by Xu Wan and 4 other authors View PDF HTML (experimental) 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, actio...
