Keywords: Reinforcement Learning, Patterns, Structure, Decomposability
TL;DR: A novel framework to understand how methods in RL incorporate structure and what it means future research.
Abstract: Reinforcement Learning (RL), empowered by Deep Neural Networks (DNNs) for function approximation, has achieved notable success in diverse applications. However, its applicability to real-world scenarios with complex dynamics, noisy signals, and large state and action spaces remains limited due to challenges in data efficiency, generalization, safety guarantees, and interpretability, among other factors. To overcome these challenges, 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 that address different auxiliary objectives. By leveraging this comprehensive framework, we provide valuable insights into the challenges of integrating structure into 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 better handle real-world scenarios. A larger and more comprehensive overview of this work can be found in our preprint at https://arxiv.org/abs/2306.16021.