Reinforcement Learning (RL), one of the core paradigms in machine learning, learns to make decisions based on real-world experiences. This approach has significantly advanced AI applications across various domains, notably in smart grid optimization and smart home automation. However, the proliferation of RL in these critical sectors has also exposed them to sophisticated adversarial attacks that target the underlying neural network policies, compromising system integrity. Given the pivotal role of RL in enhancing the efficiency and sustainability of smart grids and the personalized convenience in smart homes, ensuring the security of these systems is paramount. This paper aims to bolster the resilience of RL frameworks within these specific contexts, addressing the unique challenges posed by the intricate and potentially adversarial environments of smart grids and smart homes. We provide a thorough review of the latest adversarial RL threats and outline effective defense strategies tailored to safeguard these applications. Our comparative analysis sheds light on the nuances of adversarial tactics against RL-driven smart systems and evaluates the defense mechanisms, focusing on their innovative contributions, limitations, and the compromises they entail. By concentrating on the smart grid and smart home scenarios, this survey equips ML developers and researchers with the insights needed to secure RL applications against emerging threats, ensuring their reliability and safety in our increasingly connected world.

翻译：强化学习作为机器学习的核心范式之一，通过真实经验学习决策制定。该方法显著推动了各领域AI应用的发展，尤其在智能电网优化和智能家居自动化方面表现突出。然而，这些关键领域强化学习的广泛部署也使其面临针对底层神经网络策略的高级对抗性攻击威胁，可能导致系统完整性受损。鉴于强化学习在提升智能电网效率与可持续性、实现智能家居个性化便捷服务中的关键作用，确保这些系统的安全性至关重要。本文旨在增强特定场景下强化学习框架的韧性，应对智能电网与智能家居复杂且潜在对抗环境带来的独特挑战。我们全面综述了最新的对抗性强化学习威胁，并提出了针对这些应用场景量身定制的有效防御策略。通过对比分析，我们揭示了针对强化学习驱动智能系统的对抗性攻击战术特征，并评估了防御机制，重点关注其创新贡献、局限性及性能权衡。本综述聚焦智能电网与智能家居场景，为机器学习开发者和研究人员提供了在日益互联的世界中确保强化学习应用可靠性、安全性所需的关键洞察，以抵御新兴威胁。