  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.
