Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principles
Authors: Xianghua Zeng, Hao Peng, Angsheng Li
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations on the Star Craft II micromanagement benchmark demonstrate that, compared with state-of-the-art MARL algorithms, the SR-MARL framework improves the average test win rate by 0.17%, 6.08%, and 3.24%, and reduces the deviation by 16.67%, 30.80%, and 66.30%, under easy, hard, and super hard scenarios. |
| Researcher Affiliation | Academia | Xianghua Zeng1, Hao Peng1, Angsheng Li1, 2 1 State Key Laboratory of Software Development Environment, Beihang University, Beijing, China; 2 Zhongguancun Laboratory, Beijing, China. {zengxianghua, penghao, angsheng}@buaa.edu.cn, liangsheng@gmail.zgclab.edu.cn. |
| Pseudocode | Yes | Algorithm 1: The Sparsification Algorithm |
| Open Source Code | Yes | All source code and data are available at Github2. 2https://github.com/RingBDStack/SR-MARL |
| Open Datasets | Yes | We evaluate the SR-MARL on the Star Craft II micromanagement (SMAC) benchmark (Samvelyan et al. 2019), a mainstream benchmark of CTDE algorithms, of its rich environment and high control complexity. |
| Dataset Splits | No | The paper mentions 'test win rates' and 'average test win rates' but does not specify exact training/validation/test splits with percentages, sample counts, or explicit cross-validation details for reproducibility. |
| Hardware Specification | Yes | All experiments adopt the default settings and are conducted on 3.00GHz Intel Core i9 CPU and NVIDIA RTX A6000 GPU. |
| Software Dependencies | No | The implementations of the SR-MARL and baselines in our experiments are based on the Py MARL ((Samvelyan et al. 2019)), and the hyperparameters of the baselines have been fine-tuned on the SMAC benchmark. The paper mentions PyMARL but does not provide specific version numbers for it or any other software dependencies like Python, PyTorch, or TensorFlow. |
| Experiment Setup | No | The paper mentions that hyperparameters were 'fine-tuned' for baselines and that 'default settings' were adopted. However, it does not provide specific concrete hyperparameter values (e.g., learning rate, batch size) or detailed system-level training configurations for the experiments. |