Failure-Based Testing for Deep Reinforcement Learning Agents

2026-03-24 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

Prior Random Testing (PRT) is a novel black-box failure-based testing method for Deep Reinforcement Learning (DRL) agents, introduced on 2026-03-24. It addresses the ineffectiveness of reward-guided testing for well-trained agents, where high reward signals offer little insight into failures. PRT utilizes "task-induced failure insights" to prioritize failure-prone regions of the input domain, thereby enhancing failure detection while reducing the number of tests. The method employs a two-mechanism approach: dimension reduction to identify sparse regions and local recombination to refine candidate sets. Evaluated on four benchmarks against leading fuzzing, search-based, and generative methods, PRT consistently ranks among top performers. It notably reduces testing cost by over 50% compared to random testing and achieves superior test case diversity. PRT also demonstrates a time complexity of O(MN^2) for generating N test cases in an M-dimensional domain.

Key takeaway

For MLOps Engineers deploying well-trained DRL agents, traditional reward-guided testing is often insufficient. You should adopt failure-based methods like PRT, which utilizes task-induced failure insights to efficiently uncover critical failures. This approach significantly reduces testing costs and improves test case diversity, especially in environments where reward signals are uninformative. Consider defining failure-prone regions and using the ℱ mapping to customize testing priorities for your specific DRL applications.

Key insights

Task-induced failure insights and uniform distribution enhance DRL agent testing by prioritizing failure-prone regions.

Principles

• DRL agent failures correlate with task difficulty.
• Prioritize difficult tasks for efficient failure detection.
• Uniform test case distribution aids broad failure discovery.

Method

PRT uses dimension reduction to find sparse regions and local recombination to refine candidate sets, guided by task-induced failure insights and a confidence hyperparameter λ.

In practice

• Identify failure-prone input domain regions for DRL agents.
• Design a mapping ℱ to shift testing priority.
• Set confidence λ to control focus on failure-prone values.

Topics

• Deep Reinforcement Learning
• Failure-Based Testing
• Test Case Generation
• DRL Agent Reliability
• Autonomous Driving
• Robotic Control

Code references

• lizhuo-1994/mdp_testing
• soarsmu/CureFuzz
• QuentinMaz/QD_Based_Testing_RL
• Avagnes/PRT-DRL-Experiments

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.