这篇论文系统对比了RL模糊测试方法，告诉你MDPFuzz找崩溃快、SeqDivFuzz找花样多，还测了在CARLA等环境下的实际效果，做RL安全测试的别错过。
这篇论文对强化学习智能体的模糊测试方法进行了首次全面实证研究，从有效性、多样性、效率和实用性四个维度评估了五种最先进方法（MDPFuzz、SeqDivFuzz等）和随机测试。实验在MountainCar、BipedalWalker和CARLA三个复杂度递增的环境中进行统一配置。结果显示，MDPFuzz等吞吐量导向方法在发现崩溃方面更有效，而SeqDivFuzz等鼓励探索的方法更擅长发现多样化的崩溃行为。模糊测试产生的崩溃可有意义地提升智能体鲁棒性，并支持准确的跨方法泛化安全监控。
Evaluating Fuzz Testing for Reinforcement Learning Agents
Reinforcement Learning (RL) agents are increasingly deployed in safety-critical domains such as robotics, autonomous driving, and drone control, where unexpected behaviors may lead to severe real-world consequences. Fuzz testing has recently emerged as a promising method for exploring the vast state spaces of RL agents and exposing crashes. Although numerous RL fuzzing methods have been proposed, existing studies often differ in evaluation settings, baselines, and metrics, making it difficult to draw reliable conclusions about their relative effectiveness and practical usefulness. To address this gap, we present the first comprehensive empirical study that systematically evaluates RL fuzzing methods from four complementary perspectives: effectiveness, diversity, efficiency, and practical utility. We benchmark five state-of-the-art methods alongside random testing under unified configurations across three environments of increasing complexity (MountainCar, BipedalWalker, and CARLA), and further assess the downstream usefulness of detected crashes for agent robustness improvement and safety monitoring. Our results reveal several key insights. For instance,throughput-oriented methods like MDPFuzz demonstrate superior effectiveness and efficiency in crash discovery, while methods explicitly designed to encourage exploration like SeqDivFuzz excel at uncovering diverse crash behaviors. We also show that fuzzing-generated crashes can meaningfully improve agent robustness and enable accurate safety monitoring with strong cross-method generalization. Beyond these empirical findings, we distill actionable guidance for both researchers and practitioners, highlighting the benefits of combining complementary fuzzing strategies and adopting multi-level diversity analysis to achieve more comprehensive and practical RL testing.