Last-iterate Convergence in Regularized Graphon Mean Field Game
Authors: Jing Dong, Baoxiang Wang, Yaoliang Yu
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we verify the performance of the studied algorithms by empirically testing them against fictitious play in a variety of tasks. ... We validate the effectiveness of the studied algorithm by empirically comparing them against the fictitious play in four different environments. ... Figure 1: Experimental results for the mean field games described. |
| Researcher Affiliation | Academia | Jing Dong1,*, Baoxiang Wang1,3, , Yaoliang Yu2,3, 1 Chinese University of Hong Kong, Shenzhen, 2 University of Waterloo, 3 Vector Institute EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Tabular online mirror descent for λ-regularized GMFG |
| Open Source Code | No | No explicit statement or link for the authors' own code is provided. The mentions of 'Open Spiel' refer to a third-party implementation used for environments, not the authors' source code. For example: 'We use the setup of fictitious play from (Perrin et al. 2020) and follow the implementation from Open Spiel.' |
| Open Datasets | Yes | For all the environments described below, we use the Open Spiel implementation of the games. Predator Prey, Crowd Avoidance, Crowd modeling, and Periodic Aversion. ... Crowd Modeling ... (Perrin et al. 2020) ... Predator Prey ... (P erolat et al. 2022) ... Periodic Aversion ... (Almulla, Ferreira, and Gomes 2017) |
| Dataset Splits | No | The paper evaluates algorithms in simulated game environments (Predator Prey, Crowd Avoidance, Crowd modeling, Periodic Aversion) which are not typically partitioned into training, validation, and test sets in the traditional supervised learning sense. Therefore, no specific dataset split information is provided. |
| Hardware Specification | No | We ran all experiments with a 10-core CPU, with 32 GB memory. This mentions core count and memory amount but lacks a specific CPU model or type, falling short of the detailed hardware specification requirement. |
| Software Dependencies | No | The paper mentions using 'Open Spiel' for the implementation of the game environments but does not provide specific version numbers for Open Spiel or any other software libraries or dependencies. |
| Experiment Setup | Yes | For all of our experiments, we choose the learning rate to be ηt = 0.1 and the exploration rate γt = 0.1. We repeat the experiments with 5 different random seeds. |