SPD: Synergy Pattern Diversifying Oriented Unsupervised Multi-agent Reinforcement Learning
Authors: Yuhang Jiang, Jianzhun Shao, Shuncheng He, Hongchang Zhang, Xiangyang Ji
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show the capacity of SPD to acquire meaningful coordination policies, such as maintaining specific formations in Multi-Agent Particle Environment and passand-shoot in Google Research Football. Furthermore, we demonstrate that the same instructive pretrained policy s parameters can serve as a good initialization for a series of downstream tasks policies, achieving higher data efficiency and outperforming state-of-the-art approaches in Google Research Football. |
| Researcher Affiliation | Academia | Yuhang Jiang , Jianzhun Shao , Shuncheng He, Hongchang Zhang, Xiangyang Ji Department of Automation Tsinghua University, Beijing, China |
| Pseudocode | Yes | Algorithm 1 SPD |
| Open Source Code | Yes | Our code is available at https://github.com/thu-rllab/SPD. |
| Open Datasets | Yes | We first train SPD on the complicated MARL environment: Google Research Football [18] without environment reward. ... we first evaluate the diversity of coordination policies learned by SPD and URL baselines in Multi-agent Particle Environment2 [22, 45]. |
| Dataset Splits | No | The paper describes the scenarios and environments used but does not specify explicit train/validation/test splits or their proportions. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper mentions software components like QMIX, Sinkhorn-Knopp algorithm, and Kuhn Munkres algorithm, but it does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The hyper-parameters are kept to be the same, and please refer to Appendix B.2 for details. |