The Societal Implications of Deep Reinforcement Learning
Authors: Jess Whittlestone, Kai Arulkumaran, Matthew Crosby
JAIR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we review recent progress in DRL, discuss how this may introduce novel and pressing issues for society, ethics, and governance, and highlight important avenues for future research to better understand DRL s societal implications. |
| Researcher Affiliation | Academia | Jess Whittlestone EMAIL Leverhulme Centre for the Future of Intelligence University of Cambridge Kai Arulkumaran EMAIL Imperial College London Matthew Crosby EMAIL Leverhulme Centre for the Future of Intelligence Imperial College London |
| Pseudocode | No | The paper includes figures (Figure 1 and Figure 2) which are diagrams illustrating concepts, but no structured pseudocode or algorithm blocks are present. |
| Open Source Code | No | The paper is a review and conceptual discussion and does not describe a specific methodology for which code would typically be released. There is no statement within the paper indicating the release of source code or a link to a code repository for the work described. |
| Open Datasets | No | This paper is a review and does not conduct experiments using specific datasets. While it discusses DRL's use of data, it does not provide access information for any dataset used in its own analysis. |
| Dataset Splits | No | This paper is a review and theoretical discussion. It does not present experimental results that would require dataset splits for reproducibility. |
| Hardware Specification | No | The paper is a review and theoretical discussion and does not describe any experiments conducted by the authors. Therefore, it does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper is a review and theoretical discussion and does not describe any specific software or libraries with version numbers used for conducting experiments or implementing a methodology. |
| Experiment Setup | No | The paper is a review and theoretical discussion, focusing on the societal implications of DRL. It does not describe any experimental setup, hyperparameters, or training configurations. |