Dealing with Non-Stationarity in MARL via Trust-Region Decomposition
Authors: Wenhao Li, Xiangfeng Wang, Bo Jin, Junjie Sheng, Hongyuan Zha
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS This section aims to verify the effectiveness of the trust-region constraints, the existence of the trust-region decomposition dilemma, and the capacity of the TRD-Net with 4 cooperative tasks Spread, Multi-Walker, Rover-Tower, Pursuit (more details are in the appendix). |
| Researcher Affiliation | Academia | School of Computer Science and Technology East China Normal University Shanghai, China {52194501026@stu, xfwang@cs, bjin@cs, 52194501003@stu}.ecnu.edu.cn Hongyuan Zha School of Data Science, The Chinese University of Hong Kong (Shenzhen) Shenzhen Institute of Artiļ¬cial Intelligence and Robotics for Society Shenzhen, China zhahy@cuhk.edu.cn |
| Pseudocode | Yes | Algorithm 1 MAMT |
| Open Source Code | Yes | The source code of this paper is available at https:// anonymous.4open.science/r/MAMT. |
| Open Datasets | No | The paper describes environments (e.g., Spread, Multi-Walker, Rover-Tower, Pursuit) but does not provide concrete access information (links, DOIs, formal citations for dataset download) for them as publicly available datasets. |
| Dataset Splits | No | The paper does not explicitly state training/validation/test dataset splits (e.g., percentages or counts) for reproduction. It describes simulation environments. |
| Hardware Specification | Yes | The hardware used in the experiment is a server with 128G memory and 4 NVIDIA 1080Ti graphics cards with 11G video memory. |
| Software Dependencies | No | The paper refers to using PyTorch and other related libraries through the provided codebases, but does not explicitly list specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9') for its own implementation. |
| Experiment Setup | Yes | Table 3: Default settings of our methods used in experiments. |