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..
Learning Mean-Field Games
Authors: Xin Guo, Anran Hu, Renyuan Xu, Junzi Zhang
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on repeated Ad auction problems demonstrate that this GMF-Q algorithm is efficient and robust in terms of convergence and learning accuracy. |
| Researcher Affiliation | Academia | Xin Guo University of California, Berkeley EMAIL Anran Hu University of California, Berkeley EMAIL Renyuan Xu University of California, Berkeley EMAIL Junzi Zhang Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 Q-learning for GMFGs (GMF-Q) |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | No | The paper describes a simulated repeated Ad auction game with specific parameters, rather than using a publicly available or open dataset. No concrete access information for a dataset is provided. |
| Dataset Splits | No | The paper describes a simulated environment but does not specify training, validation, or test dataset splits in the context of typical machine learning dataset partitioning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | Parameters. The model parameters are set as: |S| = |A| = 10, the overbidding penalty ρ = 0.2, the distributions of the conversion rate v uniform({1, 2, 3, 4}), and the competition intensity index M = 5. The random fulfillment is chosen as: if s < smax, (s) = 1 with probability 1/2 and (s) = 0 with probability 1/2; if s = smax, (s) = 0. The algorithm parameters are (unless otherwise specified): the temperature parameter c = 4.0, the discount factor γ = 0.8, the parameter h from Lemma 8 in the Appendix being h = 0.87, and the baseline inner iteration being 2000. |