Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural Networks (DNNs) for function approximation, has demonstrated considerable success in numerous applications. However, its practicality in addressing a wide range of real-world scenarios, characterized by diverse and unpredictable dynamics, noisy signals, and large state and action spaces, remains limited. This limitation stems from issues such as poor data efficiency, limited generalization capabilities, a lack of safety guarantees, and the absence of interpretability, among other factors. To overcome these challenges and improve performance across these crucial metrics, one promising avenue is to incorporate additional structural information about the problem into the RL learning process. Various sub-fields of RL have proposed methods for incorporating such inductive biases. We amalgamate these diverse methodologies under a unified framework, shedding light on the role of structure in the learning problem, and classify these methods into distinct patterns of incorporating structure. By leveraging this comprehensive framework, we provide valuable insights into the challenges associated with structured RL and lay the groundwork for a design pattern perspective on RL research. This novel perspective paves the way for future advancements and aids in the development of more effective and efficient RL algorithms that can potentially handle real-world scenarios better.

翻译：强化学习（RL）在深度神经网络（DNNs）强大函数逼近能力的支撑下，已在众多应用中展现出显著成功。然而，其在应对以多样且不可预测的动态特性、噪声信号以及庞大状态和动作空间为特征的广泛现实场景时，实用性仍然有限。这一局限源于数据效率低下、泛化能力有限、缺乏安全保障及可解释性缺失等问题。为克服这些挑战并提升上述关键指标的性能，一个富有前景的方向是将问题相关的额外结构信息融入RL学习过程。RL的各子领域已提出多种纳入此类归纳偏置的方法。我们将这些多样化方法整合至统一框架中，阐明结构在学习问题中的作用，并将这些方法分类为融入结构的标准模式。通过利用这一综合框架，我们为结构化RL面临的挑战提供了宝贵见解，并为RL研究的设计模式视角奠定基础。这一新颖视角为未来进展铺平道路，并有助于开发可能更好地应对现实场景的更高效率、更有效的RL算法。