A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning
Authors: Tom Yan, Zachary Lipton
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning |
| Researcher Affiliation | Academia | Tom Yan, Zachary Lipton Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 {tyyan, zlipton}@cmu.edu |
| Pseudocode | Yes | Algorithm 1 Hierarchical-UCB-VI (Hier-UCB-VI); Algorithm 2 Hierarchical-REGIME (Hier-REGIME) |
| Open Source Code | No | This is a theory paper that does not involve code. |
| Open Datasets | No | This is a theory paper that has no experiments section. |
| Dataset Splits | No | This is a theory paper that has no experiments section. |
| Hardware Specification | No | This is a theory paper that has no experiments section. |
| Software Dependencies | No | This is a theory paper that does not involve code. |
| Experiment Setup | No | This is a theory paper that has no experiments section. |