A Pair-Approximation Method for Modelling the Dynamics of Multi-Agent Stochastic Games
Authors: Chen Chu, Zheng Yuan, Shuyue Hu, Chunjiang Mu, Zhen Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify the descriptive power of our model (a partial differential equation) across various games through comparisons with agent-based simulation results. To illustrate our model, we consider different game configurations in our experiments and numerically solve the developed partial differential equation in those games. |
| Researcher Affiliation | Academia | 1 School of Statistics and Mathematics, Yunnan University of Finance and Economics 2 School of Artificial Intelligence, OPtics and Electro Nics (i OPEN), Northwestern Polytechnical University 3 Shanghai Artificial Intelligence Laboratory 4 School of Cybersecurity, Northwestern Polytechnical University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | More details about the derivation of Equation (12) are presented in our supplementary material1. 1https://github.com/Zheng-YZ/AAAI2023SM |
| Open Datasets | No | The paper describes generating synthetic data for simulations (e.g., 'initial Q-values of agents follow different Beta distributions') but does not provide access information for a publicly available or open dataset. It refers to game configurations and simulation parameters rather than external datasets. |
| Dataset Splits | No | The paper describes simulation setups with different initial conditions but does not specify training, validation, or test dataset splits, as it primarily relies on agent-based simulations rather than pre-partitioned datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or cloud computing specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or library versions needed to replicate the experiment. |
| Experiment Setup | Yes | For the agent-based simulations, we set the population size n = 1000, the learning rate α = 0.4, and the temperature τ = 2 (Unless otherwise specified, the parameters are set in the same way for subsequent experiments). ... In (b), we set Q0(a1) Beta(20, 80, -0.1, 1.2), Q0(a2) Beta(80, 20, -0.1, 1.2). |