Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach
Authors: Christian Fabian, Kai Cui, Heinz Koeppl
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate its learning capabilities on both synthetic graphs and real-world networks. This comparison shows that our GXMFG learning algorithm successfully extends MFGs to a highly relevant class of hard, realistic learning problems that are not accurately addressed by current MARL and MFG methods. |
| Researcher Affiliation | Academia | Christian Fabian, Kai Cui & Heinz Koeppl Dept. of Electrical Engineering and Information Technology, Technische Universität Darmstadt {christian.fabian, heinz.koeppl}@tu-darmstadt.de |
| Pseudocode | Yes | Algorithm 1 Hybrid Online Mirror Descent (HOMD) |
| Open Source Code | Yes | The code is available in the supplementary material. |
| Open Datasets | Yes | We consider the following real world networks from the KONECT database (Kunegis, 2013): Prosper loans (Redmond & Cunningham, 2013), Dogster (Kunegis, 2013), Pokec (Takac & Zabovsky, 2012), Livemocha (Zafarani & Liu, 2009), Flickr (Mislove et al., 2007), Brightkite (Cho et al., 2011), Facebook (Viswanath et al., 2009), and Hyves (Zafarani & Liu, 2009). |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It mentions simulation on synthetic and real networks, and applying learned equilibria, but no details on how the data was partitioned for training, validation, or testing. |
| Hardware Specification | No | The paper states: 'The authors acknowledge the Lichtenberg high performance computing cluster of the TU Darmstadt for providing computational facilities for the calculations of this research.' This identifies a computing cluster but lacks specific hardware details such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. It mentions 'Python' implicitly through code availability but no other libraries or solvers with versions. |
| Experiment Setup | Yes | For the implementation of Algorithm 1, we use an inverse step size (temperature) of γ = 50 and perform 5000 iterations as seen in Figure 2. As degree cutoffs for the periphery, we use kmax = 8 for SIS and SIR, and kmax = 6 for RS... In our experiments, we use τI = 0.2, τR = 0.05, T = 500, µ0(I) = 0.5, c I = 1 and c P = 0.5. |