In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in machine learning due to its successful applications to real-world and complex systems. However, even the state-of-the-art DRL models have been shown to suffer from reliability concerns -- for example, their susceptibility to adversarial inputs, i.e., small and abundant input perturbations that can fool the models into making unpredictable and potentially dangerous decisions. This drawback limits the deployment of DRL systems in safety-critical contexts, where even a small error cannot be tolerated. In this work, we present a comprehensive analysis of the characterization of adversarial inputs, through the lens of formal verification. Specifically, we introduce a novel metric, the Adversarial Rate, to classify models based on their susceptibility to such perturbations, and present a set of tools and algorithms for its computation. Our analysis empirically demonstrates how adversarial inputs can affect the safety of a given DRL system with respect to such perturbations. Moreover, we analyze the behavior of these configurations to suggest several useful practices and guidelines to help mitigate the vulnerability of trained DRL networks.

通过正式验证的视角，我们对敌对输入的特征进行了全面分析，提出了一种新的指标——敌对率，用于划分模型对此类扰动的敏感性，并提供了计算该指标的一系列工具和算法。我们的分析在实证上展示了敌对输入如何影响给定深度强化学习系统在敏感性方面的安全性，并分析了这些配置的行为，提出了一些有用的实践和指导方针，以帮助减轻训练深度强化学习网络的脆弱性。

深度强化学习中对抗输入的分析