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..
Stochastic Variance Reduction Methods for Policy Evaluation
Authors: Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on benchmark problems demonstrate the effectiveness of our methods. |
| Researcher Affiliation | Collaboration | 1Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA. 2Microsoft Research, Redmond, Washington 98052, USA. |
| Pseudocode | Yes | Algorithm 1 PDBG for Policy Evaluation |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the methodology described. |
| Open Datasets | Yes | In the ο¬rst task, we consider a randomly generated MDP with 400 states and 10 actions (Dann et al., 2014). ... Next, we test these algorithms on Mountain Car (Sutton & Barto, 1998, Chapter 8). |
| Dataset Splits | No | The paper mentions using a 'fixed, finite dataset' but does not provide specific details on training, validation, or test dataset splits or percentages. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | For step size tuning, Ο is chosen from 4e-1, 1e-2, ..., 1e-6 / (1 + lambda_max(C_hat)) and Οw is chosen from 4e-1, 1e-1, 1e-2 / lambda_max(C_hat). ... for SVRG we choose N = 2n. ... We chose Ξ³ = 0.95. |