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.
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation
Authors: Chris Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 30,200 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,165 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 | This paper presents a theoretical analysis of such policies and provides the ο¬rst regret and sample-complexity bounds for reinforcement learning with myopic exploration. |
| Researcher Affiliation | Collaboration | 1Google Research 2Tel Aviv University 3Courant Institute of Mathematical Sciences 4Cornell University. |
| Pseudocode | Yes | Algorithm 1: RL with myopic exploration |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is open or available. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any specific publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not mention any specific hardware used for experiments, consistent with a theoretical work. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe a concrete experimental setup with hyperparameters or system-level training settings. |