Automatic Grouping for Efficient Cooperative Multi-Agent Reinforcement Learning
Authors: Yifan Zang, Jinmin He, Kai Li, Haobo Fu, Qiang Fu, Junliang Xing, Jian Cheng
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the Star Craft II micromanagement tasks and Google Research Football scenarios verify our method s effectiveness. Extensive component studies show how grouping works and enhances performance. |
| Researcher Affiliation | Collaboration | Yifan Zang1,2, Jinmin He1,2, Kai Li1,2 , Haobo Fu3, Qiang Fu3, Junliang Xing4, Jian Cheng1,2 1Institute of Automation, Chinese Academy of Sciences 2School of Artiļ¬cial Intelligence, University of Chinese Academy of Sciences 3Tencent AI Lab, 4Tsinghua University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The detailed setting of Go MARL s hyperparameters is shown in our source code1. 1https://github.com/zyfsjycc/Go MARL |
| Open Datasets | Yes | We compare all the methods in six Hard and Super Hard Star Craft II micromanagement tasks (SMAC) [29] and three challenging Google Research Football (GRF) [16] scenarios. |
| Dataset Splits | No | The paper mentions training and evaluation episodes, but does not provide specific training/validation/test dataset splits in terms of percentages or counts, which is typical for reinforcement learning environments where data is generated dynamically. |
| Hardware Specification | Yes | We use one NVIDIA Titan V GPU for training. |
| Software Dependencies | No | The paper mentions using the Py MARL2 framework but does not specify version numbers for PyMARL or any other software dependencies. |
| Experiment Setup | Yes | All the methods are trained with 8 parallel runners for 10M steps and are evaluated every 10K steps with 32 episodes. |