Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 15,127 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,068 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |