Multi-Player Zero-Sum Markov Games with Networked Separable Interactions
Authors: Chanwoo Park, Kaiqing Zhang, Asuman Ozdaglar
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also provide numerical experiments to corroborate our theoretical results. |
| Researcher Affiliation | Academia | Chanwoo Park MIT cpark97@mit.edu Kaiqing Zhang University of Maryland, College Park kaiqing@umd.edu Asuman Ozdaglar MIT asuman@mit.edu |
| Pseudocode | Yes | The overall dynamics are summarized in Algorithm 4... The overall procedure is summarized in Algorithm 6. ... We propose to study the vanilla Multiplicative Weight Update (MWU) algorithm [55] in the regularized zero-sum NG, as tabulated in Algorithm 9. We have also introduced a variant with diminishing regularization, and summarize the update rule in Algorithm 10. |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code for the methodology described. |
| Open Datasets | No | The paper introduces examples like 'Markov fashion games' and mentions 'numerical experiments', but does not specify the use of any publicly available datasets or provide concrete access information (link, DOI, citation) for data used in training or evaluation. |
| Dataset Splits | No | The provided text does not contain specific information about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |