Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition
Authors: Shunyu Liu, Yihe Zhou, Jie Song, Tongya Zheng, Kaixuan Chen, Tongtian Zhu, Zunlei Feng, Mingli Song
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the SMAC benchmarks and across different VD backbones demonstrate that the proposed method yields results superior to the state-of-the-art counterparts. |
| Researcher Affiliation | Academia | Zhejiang University liushunyu@zju.edu.cn, yihe zhou@zju.edu.cn, sjie@zju.edu.cn, tyzheng@zju.edu.cn, chenkx@zju.edu.cn, raiden@zju.edu.cn, zunleifeng@zju.edu.cn, brooksong@zju.edu.cn |
| Pseudocode | Yes | To make the proposed CIA clearer to readers, we provide the pseudocode in Appendix A. |
| Open Source Code | Yes | Our code is available at https://github.com/liushunyu/CIA. |
| Open Datasets | Yes | We conduct experiments on the didactic game and the Star Craft II micromanagement challenge. ... The Star Craft Multi-Agent Challenge (SMAC)2 (Samvelyan et al. 2019) has become a common-used benchmark for evaluating state-of-the-art MARL methods. |
| Dataset Splits | No | The paper uses the SMAC benchmark but does not explicitly provide details about training, validation, and test dataset splits (e.g., percentages or counts for each split). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper mentions 'Python MARL framework (Py MARL)' but does not specify its version or any other software dependencies with version numbers, except for the game environment version 'SC2.4.10'. |
| Experiment Setup | Yes | The detailed hyperparameters are given in Appendix B, where the common training parameters across different methods are consistent. |