Structure in Deep Reinforcement Learning: A Survey and Open Problems

TL;DR

The paper argues that deep reinforcement learning (RL) struggles with data efficiency, generalization, safety, and interpretability in real-world settings. It proposes a unifying framework that treats structure as side information, decomposing problems into latent, factored, relational, and modular archetypes and organizing methods into seven repeatable design patterns. By connecting these decompositions with patterns—such as abstraction, augmentation, auxiliary optimization, and environment generation—the authors provide a principled lens to analyze existing work and guide future research. The work also outlines open problems across offline and unsupervised RL, foundation models, partial observability, AutoRL, and meta-RL, emphasizing a pattern-driven roadmap for scalable, robust, and interpretable structured RL. Overall, the framework aims to standardize design decisions around problem structure to accelerate practical advances in RL and bridge theory with real-world deployment.

Abstract

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.

Figures (12)

• Figure 1: Overview of our framework. Side information can be used to achieve improved performance across metrics such as Sample Efficiency, Generalization, Interpretability, and Safety. We discuss this process in \ref{['sec:usage']}. A particular source of side information is decomposability in a learning problem, which can be categorized into four archetypes along a spectrum - Latent, Factored, Relational, and Modular - explained further in \ref{['sec:Structure:types']}. Incorporating side information about decomposability amounts to adding structure to a learning pipeline, and this process can be categorized into seven different patterns - Abstraction, Augmentation, Auxiliary Optimization, Auxiliary Model, Warehouse, Environment Generation, and Explicitly Designed - discussed further in \ref{['sec:patterns']}.
• Figure 2: The anatomy of an RL pipeline.
• Figure 3: Spectrum of Decomposability and Structural Archetypes. On the left end of the spectrum exist monolithic structural decompositions where a latent representation of $\mathcal{X}$ can be learned and incorporated as an inductive bias. Moving towards the right, we can learn multiple latent representations, albeit in a monolithic solution. These are factored representations. Further ahead, we see the emergence of interactionally complex decompositions, where knowledge about factorization and how they relate to each other might be essential and can be incorporated using relational representations. Finally, we see fully distributed subsystems that can be learned using individual policies. We call these modular representations.
• Figure 4: Patterns of incorporating structural information. We categorize the methods of incorporating structure as inductive biases into the learning pipeline into patterns that can be applied for different kinds of usages. Each pattern is shown as a plug-and-play modification of the RL pipeline $\Omega$ that aims to improve the performance of $\Omega$ on one or more objectives discussed in \ref{['sec:usage']}.
• Figure 5: Proclivities. A meta-analysis of the proclivities of each pattern to the additional objectives. The four additional objectives covered in this text are on the x-axis. We show each objective's share percentage of publications utilizing individual patterns. This data has been shown on the y-axis with different colors for each pattern. Therefore, this figure helps us understand correlations between patterns and the kind of objectives they address.