T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration
Authors: Chuxiong Sun, Zehua Zang, Jiabao Li, Jiangmeng Li, Xiao Xu, Rui Wang, Changwen Zheng
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on a diverse set of cooperative multi-agent tasks, with varying difficulties, involving different scales and ranging from Hallway, MPE to SMAC. The experiments indicate that the proposed model not only surpasses the stateof-the-art methods in terms of cooperative performance and communication efficiency, but also exhibits impressive generalization. |
| Researcher Affiliation | Academia | 1Science & Technology on Integrated Information System Laboratory, Institute of Software Chinese Academy of Sciences 2State Key Laboratory of Intelligent Game 3University of Chinese Academy of Sciences 4School of Automation and Electrical Engineering, University of Science and Technology Beijing |
| Pseudocode | Yes | Algorithm 1: T2MAC |
| Open Source Code | No | The paper does not explicitly state that open-source code for the methodology is provided, nor does it include a link to a code repository. |
| Open Datasets | Yes | We subjected T2MAC to rigorous testing across various MARL environments, such as Hallway, MPE, and SMAC. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, and test dataset splits in terms of percentages, sample counts, or explicit splitting methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers. |
| Experiment Setup | Yes | To ensure transparency and reproducibility, the intricate details of our method s architecture and our hyperparameter choices are extensively detailed in Table 1. Module Architecture: Obs Encoder Linear(obs dim, 64) Linear(64, 64) Linear(64, 64) RNN(64, 64) Evidence Encoder n*Linear(64, K) Selector Network Linear(64, n) |