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..
Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation
Authors: Yudan Wang, Yue Wang, Yi Zhou, Shaofeng Zou
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results are also provided in the appendix. In this section, we conduct experiments to numerically verify our AC/NAC with compatible function approximation. |
| Researcher Affiliation | Academia | 1Electrical Engineering, University at Buffalo 2Electrical and Computer Engineering, University of Central Florida 3Electrical and Computer Engineering, University of Utah 4Computer Science & Engineering, University at Buffalo. |
| Pseudocode | Yes | Algorithm 1 (Natural) Actor-Critic with Compatible Function Approximation; Algorithm 2 Compatible k-step TD Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We test our algorithms in the Acrobot environment (Sutton, 1995). |
| Dataset Splits | No | The paper uses the Acrobot environment but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | In our experiment setup, we set k = 128, and design a 2-layer neural network with 16 hidden neurons to represent the policy, which contains 163 parameters. |