Harnessing Density Ratios for Online Reinforcement Learning
Authors: Philip Amortila, Dylan J Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | While our results are theoretical in nature, we are optimistic that they will lead to further investigation into the power of density ratio modeling in online RL and inspire practical algorithms. |
| Researcher Affiliation | Collaboration | Philip Amortila UIUC Dylan J. Foster Microsoft Research Nan Jiang UIUC Ayush Sekhari MIT Tengyang Xie Microsoft Research |
| Pseudocode | Yes | Algorithm 1 GLOW: Global Optimism via Weight Function Realizability |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper presents theoretical results and algorithms (GLOW, HYGLOW, H2O) and does not involve empirical evaluation on datasets. Therefore, it does not mention dataset availability for training. |
| Dataset Splits | No | The paper is theoretical and does not report on empirical experiments with datasets. Therefore, it does not provide information about training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not conduct empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental implementation details that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations for empirical evaluation. |