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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Fine-grained Analysis of Fitted Q-evaluation: Beyond Parametric Models
Authors: Jiayi Wang, Zhengling Qi, Raymond K. W. Wong
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a simulation study to illustrate the behavior of the error |ˆν(π) ν(π)| with respect to n and T. The goal here is to provide empirical evidence of our theoretical results, and so we use a relatively simple simulation setup for the purpose of clear demonstration. |
| Researcher Affiliation | Academia | 1Department of Mathematical Sciences, University of Texas at Dallas, Richardson, USA 2School of Business, The George Washington University, Washington, D.C., USA 3Department of Statistics, Texas A&M University, College Station, USA. |
| Pseudocode | No | The paper describes the FQE method and provides mathematical formulations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about making its source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper uses synthetic data generated via a specified model rather than a publicly available dataset. It describes: 'The state variable is a one-dimensional continuous variable and the action is a binary variable, i.e., At = {0, 1} for all t. The initial state follows the uniform distribution within [ 2, 2]. The transition dynamics are given by Si,t+1 = (2Ai,t 1)f(Si,t)...' |
| Dataset Splits | No | The paper describes the use of leave-one-out cross-validation to decide the number of basis functions (K) but does not specify any explicit training, validation, or test dataset splits for the simulated data. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, or memory) used to run the simulation studies. |
| Software Dependencies | No | The paper mentions using 'cubic B-spline' for constructing basis functions but does not specify any software packages or their version numbers used for implementation or analysis. |
| Experiment Setup | Yes | We conduct a simulation study to illustrate the behavior of the error |ˆν(π) ν(π)| with respect to n and T. ... We evaluate values with n = 200, 400, . . . , 2000, and T = 20, 40, . . . , 200. We use cubic B-spline to construct basis functions at every step t. The knots are placed at evenly distributed percentiles of samples. ... we fix K = 3n1/5. For the second approach, we use leave-one-out cross-validation to decide K at every step. |