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.
PAC Reinforcement Learning for Predictive State Representations
Authors: Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 41,099 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,588 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 | Our algorithm naturally works with function approximation to extend to systems with potentially large state and observation spaces. We show that given a realizable model class, the sample complexity of learning the near optimal policy only scales polynomially with respect to the statistical complexity of the model class, without any explicit polynomial dependence on the size of the state and observation spaces. Notably, our work is the ο¬rst work that shows polynomial sample complexities to compete with the globally optimal policy in PSRs. |
| Researcher Affiliation | Academia | Anonymous authors Paper under double-blind review |
| Pseudocode | Yes | Algorithm 1 CRANE |
| Open Source Code | No | No, the paper does not provide any statement or link indicating the availability of open-source code. |
| Open Datasets | No | No, the paper is theoretical and focuses on sample complexity. It does not describe experiments using specific datasets, thus no access information for training data is provided. |
| Dataset Splits | No | No, the paper is theoretical and does not describe experiments with dataset splits, thus no validation split information is provided. |
| Hardware Specification | No | No, the paper is theoretical and does not mention any hardware specifications for running experiments. |
| Software Dependencies | No | No, the paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | No, the paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |