In sequential decision making, neural networks (NNs) are nowadays commonly used to represent and learn the agent's policy. This area of application has implied new software quality assessment challenges that traditional validation and verification practises are not able to handle. Subsequently, novel approaches have emerged to adapt those techniques to NN-based policies for sequential decision making. This survey paper aims at summarising these novel contributions and proposing future research directions. We conducted a literature review of recent research papers (from 2018 to beginning of 2023), whose topics cover aspects of the test or verification of NN-based policies. The selection has been enriched by a snowballing process from the previously selected papers, in order to relax the scope of the study and provide the reader with insight into similar verification challenges and their recent solutions. 18 papers have been finally selected. Our results show evidence of increasing interest for this subject. They highlight the diversity of both the exact problems considered and the techniques used to tackle them.

翻译：在序贯决策中，神经网络（NN）目前被广泛用于表示和学习智能体的策略。这一应用领域带来了传统验证与确认实践难以应对的新型软件质量评估挑战。为此，研究者提出了多种创新方法，使相关技术适配于基于神经网络的序贯决策策略。本文综述旨在总结这些创新贡献并展望未来研究方向。我们系统回顾了2018年至2023年初的最新研究论文，其主题涵盖基于神经网络策略的测试或验证。通过从已选论文出发采用滚雪球式检索方法扩充文献范围，为读者提供相似验证挑战及其最新解决方案的深入见解。最终筛选出18篇论文。研究结果表明，该领域关注度持续上升，同时揭示了所研究具体问题及解决技术均呈现显著多样性。