Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Multi-Agent Concentrative Coordination with Decentralized Task Representation
Authors: Lei Yuan, Chenghe Wang, Jianhao Wang, Fuxiang Zhang, Feng Chen, Cong Guan, Zongzhang Zhang, Chongjie Zhang, Yang Yu
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various complex multi-agent benchmarks demonstrate that MACC achieves remarkable performance compared to existing methods. |
| Researcher Affiliation | Collaboration | 1National Key Laboratory for Novel Software Technology, Nanjing University 2Institute for Interdisciplinary Information Sciences, Tsinghua University 3Peng Cheng Laboratory 4Polixir Technologies |
| Pseudocode | No | The paper includes a "Structure of MACC" diagram (Figure 1) but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | Code available at https://github.com/Dr Zero0/MACC |
| Open Datasets | Yes | We evaluate the proposed method under environments where subtasks have different strategies (one immobile, one random moving strategy, and one fixed unknown strategy), including level-based foraging (LBF) [Papoudakis et al., 2021], predator-prey (PP) [Boehmer et al., 2020], and Star Craft II unit micromanagement benchmark (SMAC) [Samvelyan et al., 2019]. |
| Dataset Splits | No | The paper mentions environments used for evaluation but does not provide specific details on training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific GPU or CPU models) used to run its experiments. |
| Software Dependencies | No | The paper mentions being based on "Py MARL" and using "SC2.4.6.2.6923" (StarCraft II game version), but it does not specify version numbers for Py MARL or other key software components. |
| Experiment Setup | Yes | Detailed network architecture and hyperparameters choices are shown in Appendix. |