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 various real-world scenarios, characterized by diverse and unpredictable dynamics, noisy signals, and large state and action spaces, remains limited. This limitation stems from 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 of 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 developing more effective and efficient RL algorithms that can potentially handle real-world scenarios better.

翻译：强化学习（RL）借助深度神经网络（DNN）在函数逼近方面的强大表达能力，已在众多应用中取得显著成功。然而，其在应对以多样化动态、噪声信号以及庞大状态与动作空间为特征的各种现实场景时，实用性仍然有限。这种局限性源于数据效率低下、泛化能力受限、缺乏安全性保障以及可解释性缺失等因素。为克服这些挑战并提升上述关键指标的性能，一个颇具前景的途径是将问题相关的额外结构化信息融入RL学习过程。RL的多个子领域已提出融入此类归纳偏置的方法。我们将这些多样化方法融合于统一框架之下，揭示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几种典型模式。借助这一综合框架，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了基础。这一新颖视角为未来进展铺平了道路，并有助于开发更有效、更高效的RL算法，使其有望更好地应对现实世界场景。