Learning Deep Mean Field Games for Modeling Large Population Behavior
Authors: Jiachen Yang, Xiaojing Ye, Rakshit Trivedi, Huan Xu, Hongyuan Zha
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method learns both the reward function and forward dynamics of an MFG from real data, and we report the first empirical test of a mean field game model of a real-world social media population. |
| Researcher Affiliation | Academia | 1Georgia Institute of Technology 2Georgia State University |
| Pseudocode | Yes | Algorithm 1 Guided cost learning; Algorithm 2 Actor-critic algorithm for MFG |
| Open Source Code | No | The paper does not include a statement about releasing source code or a link to a code repository. |
| Open Datasets | No | We use data representing the activity of a Twitter population consisting of 406 users. Data collection proceeded as follows for 27 days... |
| Dataset Splits | No | The paper mentions 'The training set consists of trajectories...over the first M = 21 days' and 'evaluated against data from 6 held-out test days', but does not explicitly detail a separate validation set or its split. |
| Hardware Specification | No | The paper does not specify any hardware details used for running the experiments. |
| Software Dependencies | No | VAR was implemented using the Statsmodels module in Python, with order 18 selected via random sub-sampling validation with validation set size 5 (Seabold & Perktold, 2010). All layers were initialized using the Xavier normal initializer in Tensorflow. |
| Experiment Setup | Yes | Table 1: Parameters S max actor-critic episodes 4000 β critic learning rate O(1/s) ξ actor learning rate O(1/s ln ln s) c αi j scaling factor 1e4 ϵ Adam optimizer learning rate for reward 1e-4 d R convergence threshold for reward iteration 1e-4 θfinal learned policy parameter 8.64 |