In this article, we explore the validation and verification of neural network (NN) based policies for sequential decision making. We delve into the research topic of how to evaluate and ensure that these policies are reliable and accurate in making decisions. Our focus is on a more specific area of NN-based policies, which are used to solve complex problems in various fields such as finance, healthcare, and transportation.
Methodology
To conduct our study, we adopted an iterative process that involved gathering papers, selecting them based on specific criteria, and analyzing their content. We started with a small number of papers and gradually added more to our pool, ensuring that each new addition was relevant to the topic at hand. We looked for papers that described the validation and verification of NN-based policies in various contexts.
Statistical Results
We analyzed 18 papers that met our selection criteria and found some interesting trends. Firstly, we noticed that most of the papers (72%) were published between 2017 and 2020, with a slight decrease in the number of publications after 2019. Secondly, we observed that the majority of these papers (67%) focused on white-box environments, where the NN model’s architecture and parameters are known. Finally, we found that only a few papers (17%) discussed the use of reinforcement learning or deep reinforcement learning techniques in their validation and verification processes.
Review
In conclusion, our survey highlights the need for more research on the validation and verification of NN-based policies in real-world scenarios. While many papers focus on theoretical aspects of these policies, there is a lack of practical studies that demonstrate their effectiveness in different settings. We recommend future research to explore the use of reinforcement learning techniques in validating and verifying NN-based policies. By doing so, we can improve the reliability and accuracy of these policies in making decisions, which are crucial in various fields.
Limitations
Our study has some limitations that should be acknowledged. Firstly, our sample size is small compared to other surveys that cover a broader range of topics. Secondly, we focused only on peer-reviewed papers and preprints, which may not capture the entire landscape of research in this field. Finally, our analysis did not consider the impact of domain knowledge on the validation and verification processes, which could be an important factor in certain scenarios.
Future Research Directions
To address the limitations of our study, we recommend future research to explore these areas: (1) investigating the use of reinforcement learning techniques in validating and verifying NN-based policies, (2) studying the impact of domain knowledge on these processes, and (3) examining the applicability of NN-based policies in real-world scenarios. By exploring these areas, we can further understand the validation and verification of NN-based policies and improve their reliability and accuracy in making decisions.
