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 [1].
Learning Regularized Monotone Graphon Mean-Field Games
Authors: Fengzhuo Zhang, Vincent Tan, Zhaoran Wang, Zhuoran Yang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The efficiency of the designed algorithm is corroborated by empirical evaluations.In this section, we conduct experiments to corroborate our theoretical findings. |
| Researcher Affiliation | Academia | 1National University of Singapore 2 Northwestern University 3Yale University |
| Pseudocode | Yes | Algorithm 1 Mono GMFG-PMD Procedure: and Algorithm 2 Estimation of Action-value Function Procedure: |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the methodology described. |
| Open Datasets | Yes | We run different algorithms on the Beach Bar problem Perrin et al. [2020], Fabian et al. [2022]. |
| Dataset Splits | No | The paper describes sampling N agents and collecting data from K episodes for its experiments but does not provide specific train/validation/test dataset splits, such as percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions parameters like N (number of sampled players) and K (number of episodes) used in experiments, and states that 'The details of experiments are deferred to Appendix B.', but does not provide specific hyperparameter values (e.g., learning rate, batch size, optimizer settings) in the main text. |