Bounded Rationality Equilibrium Learning in Mean Field Games
Authors: Yannick Eich, Christian Fabian, Kai Cui, Heinz Koeppl
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we evaluate our algorithms and analyze the different equilibria for several MFGs. We analyze the efficacy of our methods for a Susceptible-Infectious Susceptible (SIS) problem and a sequential version of Rock-Paper-Scissor (RPS) game. Additionally, we evaluate random MFGs, similar to P erolat et al. (2022), by creating random transition and reward matrices and adding a mean-field dependent function to the reward that promotes swarm avoiding behaviour. Detailed game descriptions are found in Eich et al. (2024, Appendix G). For code, see https://github.com/yannickeich/QRE-MFG. To measure algorithm efficiency, we quantify the distance... |
| Researcher Affiliation | Academia | Yannick Eich, Christian Fabian, Kai Cui, Heinz Koeppl Dept. of Electrical Engineering and Information Technology, Technische Universit at Darmstadt EMAIL |
| Pseudocode | Yes | Algorithm 1: Generalized Fixed-Point Iteration (GFPI). Algorithm 2: Generalized Fictitious Play (GFP). Algorithm 3: Sequential RH-GFP. |
| Open Source Code | Yes | For code, see https://github.com/yannickeich/QRE-MFG. |
| Open Datasets | No | In this section we evaluate our algorithms and analyze the different equilibria for several MFGs. We analyze the efficacy of our methods for a Susceptible-Infectious Susceptible (SIS) problem and a sequential version of Rock-Paper-Scissor (RPS) game. Additionally, we evaluate random MFGs, similar to P erolat et al. (2022), by creating random transition and reward matrices and adding a mean-field dependent function to the reward that promotes swarm avoiding behaviour. Detailed game descriptions are found in Eich et al. (2024, Appendix G). |
| Dataset Splits | No | The paper does not describe typical machine learning experiments with train/test/validation splits. The experiments focus on evaluating algorithms in simulated game environments, not on partitioning an existing dataset. |
| Hardware Specification | No | The paper does not mention any specific hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers. |
| Experiment Setup | Yes | Figure 2: Convergence of GFP for the Susceptible-Infectious Susceptible MFG with α = 1.0 and β = 0.95. |