Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Towards Minimax Optimal Reward-free Reinforcement Learning in Linear MDPs
Authors: Pihe Hu, Yu Chen, Longbo Huang
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 40,095 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,552 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove an r Op H4d2{Ï”2q sample complexity upper bound for LSVI-RFE, where H is the episode length and d is the feature dimension. We also establish a sample complexity lower bound of âŠp H3d2{Ï”2q. |
| Researcher Affiliation | Academia | Pihe Hu , Yu Chen , Longbo Huang: Institute for Interdisciplinary Institute for Interdisciplinary Information Sciences Tsinghua University, Beijing, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Least-Squares Value Iteration RFE (LSVI-RFE): Exploration Phase |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not involve empirical evaluation on datasets. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical evaluation on datasets, thus no dataset splits are provided. |
| Hardware Specification | No | The paper discusses computational complexity in theoretical terms (O-notation) but does not specify any particular hardware used for experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or training configurations. |