Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
The Limits of Transfer Reinforcement Learning with Latent Low-rank Structure
Authors: Tyler Sam, Yudong Chen, Christina Yu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | There are no experiments in this paper. |
| Researcher Affiliation | Academia | Tyler Sam Cornell University EMAIL Yudong Chen University of Wisconsin-Madison EMAIL Christina Lee Yu Cornell University EMAIL |
| Pseudocode | Yes | Algorithm 1 Source Phase; Algorithm 2 Target Phase: LSVI-UCB-(S, S, d) |
| Open Source Code | No | There is no data or code used in this paper. |
| Open Datasets | No | The paper does not conduct experiments with datasets; therefore, it does not specify any training datasets or their public availability. |
| Dataset Splits | No | The paper does not conduct experiments; therefore, it does not provide validation dataset splits. |
| Hardware Specification | No | The paper does not conduct experiments; therefore, it does not describe the hardware used. |
| Software Dependencies | No | The paper does not conduct experiments; therefore, it does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not conduct experiments; therefore, it does not provide details about an experimental setup. |