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 | Conference PDF | Archive PDF | Plain Text | 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.