On Imitation in Mean-field Games
Authors: Giorgia Ramponi, Pavel Kolev, Olivier Pietquin, Niao He, Mathieu Lauriere, Matthieu Geist
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also provide a numerical illustration empirically supporting our claims in the appendix. |
| Researcher Affiliation | Collaboration | 1 ETH AI Center, Zurich 2 Max Planck Institute for Intelligent Systems, Tübingen, Germany 3 Google Deep Mind 4 ETH Zurich, Department of Computer Science 5 Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning, NYU Shanghai |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It describes concepts and equations. |
| Open Source Code | No | The paper does not provide a specific link or an explicit statement about releasing source code for the methodology described. |
| Open Datasets | No | The paper describes a simulated environment called "Attractor MFG" for its experiments, rather than using a publicly available or open dataset. No access information is provided for data. |
| Dataset Splits | No | The paper describes a simulation setup and does not mention training/validation/test dataset splits. It focuses on varying parameters of a simulated model. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its simulations. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for its simulations. |
| Experiment Setup | Yes | The experiment consists in computing the errors BCn , vanilla ADVn , and MFC ADVn for various values of L = {0.01, 0.05, 0.1, 0.5} and H = {3, 25, 50, 75, 100} and we show the NIG as a function of the mentioned errors. |