Structure in Deep Reinforcement Learning: A Survey and Open Problems
Authors: Aditya Mohan, Amy Zhang, Marius Lindauer
JAIR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. |
| Researcher Affiliation | Collaboration | Aditya Mohan EMAIL Institute of Artificial Intelligence Leibniz University Hannover Amy Zhang EMAIL University of Texas at Austin, Meta AI Marius Lindauer EMAIL Institute of Artificial Intelligence, L3S Research Center Leibniz University Hannover |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It describes concepts and frameworks but does not present a specific algorithm in a code-like format. |
| Open Source Code | No | The paper does not provide any explicit statement or link to open-source code for the methodology described. Code availability is not mentioned in the main text or acknowledgements. |
| Open Datasets | No | This paper is a survey and framework proposal, and as such, it does not conduct experiments that would use its own dataset. While it references various datasets and environments used by other researchers (e.g., 'Open AI Gym (Brockman et al., 2016)'), it does not provide concrete access information for a dataset used within this paper's own analysis. |
| Dataset Splits | No | The paper is a survey and does not present experimental results from its own methodology, therefore it does not provide dataset split information. |
| Hardware Specification | No | The paper is a survey and does not describe any experimental setup or report results that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is a survey and does not conduct experiments, hence it does not list any specific software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper is a survey and framework, not an empirical study. Therefore, it does not contain specific experimental setup details such as hyperparameter values or training configurations. |