Evaluating Fuzz Testing for Reinforcement Learning Agents

2026-07-27

AI Summary

This study addresses the critical challenge of evaluating unintended behaviors of reinforcement learning agents in safety-critical scenarios, where existing fuzzing methods lack standardized benchmarks. The authors present the first comprehensive, multi-dimensional, and cross-environment benchmarking study, systematically evaluating five state-of-the-art fuzzing approaches—including MDPFuzz and SeqDivFuzz—alongside random testing across MountainCar, BipedalWalker, and CARLA environments. Assessments span four key dimensions: effectiveness, diversity, efficiency, and practical utility. Results reveal that throughput-oriented methods like MDPFuzz excel at rapidly uncovering crashes, whereas exploration-oriented strategies such as SeqDivFuzz expose a broader spectrum of failure modes. The collected crash data substantially enhances agent robustness and enables generalizable safety monitoring. The work concludes with actionable guidelines advocating for hybrid strategies and multi-level diversity analysis to advance reliable RL evaluation.

Abstract

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.

Problem

• fuzz testing
• reinforcement learning
• evaluation
• robustness
• safety

Innovation

• RL fuzzing
• empirical evaluation
• crash discovery
• robustness improvement
• safety monitoring