Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization
Authors: Jianhao Wang, Zhizhou Ren, Beining Han, Jianing Ye, Chongjie Zhang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, to support our theoretical implications in practical realization, we conduct an empirical analysis of state-of-the-art deep multi-agent Q-learning algorithms on didactic examples and a broad set of Star Craft II unit micromanagement tasks. |
| Researcher Affiliation | Academia | Jianhao Wang1 , Zhizhou Ren2 , Beining Han1, Jianing Ye1, Chongjie Zhang1 1Institute for Interdisciplinary Information Sciences, Tsinghua University 2Department of Computer Science, University of Illinois at Urbana-Champaign |
| Pseudocode | Yes | Algorithm 1 Factorized Multi-Agent Fitted Q-Iteration (FMA-FQI) |
| Open Source Code | No | No explicit statement or link providing open-source code for the methodology described in this paper was found. |
| Open Datasets | Yes | We utilize Star Craft Multi-Agent Challenge (SMAC) benchmark [20] |
| Dataset Splits | No | The paper describes the collection of an offline dataset but does not explicitly provide training/validation/test dataset splits with specific percentages, counts, or a detailed splitting methodology for the models evaluated. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned. |
| Software Dependencies | No | No specific ancillary software details, such as library names with version numbers, were provided. |
| Experiment Setup | No | A detailed description of the experiment setting is deferred to Appendix E. |