Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning
Authors: Wei Duan, Jie Lu, Junyu Xuan
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluations, conducted on Star Craft II micromanagement tasks, demonstrate GACG s superior performance. An ablation study further provides experimental evidence of the effectiveness of each component of our method. |
| Researcher Affiliation | Academia | Wei Duan , Jie Lu , Junyu Xuan Australian Artificial Intelligence Institute (AAII), University of Technology Sydney wei.duan@student.uts.edu.au, {jie.lu, junyu.xuan}@uts.edu.au |
| Pseudocode | No | The paper describes the method using textual explanations and a framework diagram (Figure 2), but no pseudocode or explicitly labeled algorithm block is provided. |
| Open Source Code | Yes | The code is available at: https://github.com/Wei9711/GACG |
| Open Datasets | Yes | The experiments in this study are conducted using the Star Craft II benchmark [Samvelyan et al., 2019a] |
| Dataset Splits | No | The paper does not specify exact training, validation, and test dataset splits, nor does it describe a cross-validation setup. It mentions '5 random seeds' for experimental robustness but not for data partitioning. |
| Hardware Specification | No | The paper mentions 'GPUs' in a general context regarding parallelization but does not provide specific hardware details such as GPU or CPU models, processor types, or memory specifications used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation or experiments. |
| Experiment Setup | Yes | The environments are configured with a difficulty level of 7. The experiments are systematically carried out with 5 random seeds to ensure robustness and reliability in the assessment of the proposed methods... The training involves minimizing a loss function, composed of a temporal-difference (TD) loss and the group distance loss, as follows: L(θ) = LT D(θ ) + λLg (θg)... We explore different values for k, specifically {1, 5, 10, 20}. |