深度强化学习的攻防与安全性分析综述

A Survey of Attack,Defense and Related Security Analysis for Deep Reinforcement Learning

深度强化学习是人工智能领域新兴技术之一,它将深度学习强大的特征提取能力与强化学习的决策能力相结合,实现从感知输入到决策输出的端到端框架,具有较强的学习能力且应用广泛.然而,已有研究表明深度强化学习存在安全漏洞,容易受到对抗样本攻击.为提高深度强化学习的鲁棒性、实现系统的安全应用,本文针对已有的研究工作,较全面地综述了深度强化学习方法、对抗攻击、防御方法与安全性分析,并总结深度强化学习安全领域存在的开放问题以及未来发展的趋势,旨在为从事相关安全研究与工程应用提供基础.

Deep reinforcement learning is one of the emerging technologies in the field of artificial intelligence.It combines the powerful feature extraction capabilities of deep learning with the decision-making capabilities of reinforcement learning to achieve an end-to-end framework from status input to the decision output,which also makes it regarded as an important way to general artificial intelligence.However,existing studies have shown that deep reinforcement learning has security vulnerabilities and is vulnerable to adversarial sample attacks.In order to improve the robustness of deep reinforcement learning and realize the security application of the system,this article comprehensively summarizes deep reinforcement learning methods,adversarial attacks,defense methods and security analysis based on existing research work,and summarizes deep reinforcement learning security The open problems in the field and future development trends are intended to provide a basis for relevant safety research and engineering applications.