Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning
Authors: Yitian Hong, Yaochu Jin, Yang Tang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed strategy is theoretically proved and empirically verified on the Star Craft Multi-Agent Challenge benchmark problem with zero sight view. The results also confirm that the proposed method outperforms state-of-the-art IGM-based approaches. |
| Researcher Affiliation | Academia | Yitian Hong East China University of Science and Technology ythong1314@mail.ecust.edu.cn Yaochu Jin Bielefeld University yaochu.jin@uni-bielefeld.de Yang Tang East China University of Science and Technology yangtang@ecust.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of the proposed algorithm can be downloaded at *. *https://github.com/momo-xiaoyi/pymarl_HDA |
| Open Datasets | Yes | To rigorously investigate the performance of the proposed learning strategy, we adopt the Star Craft II benchmark task as the test problem. [...] The Star Craft Multi-Agent Challenge benchmark problem |
| Dataset Splits | No | The paper refers to the Star Craft II benchmark, but does not explicitly provide specific train/validation/test dataset splits, percentages, or sample counts. It mentions "implementation details and experimental settings can be found in Appendices C and D" but these appendices do not detail dataset splitting for reproduction. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU models, memory) used for running the experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For fair evaluations, the hyper-parameters of all algorithms under comparison as well as the optimizers, are the same, and the experimental results are presented with the average performance with 25-75% percentile. Moreover, the presented curves are smoothed by a moving average filter with its window size being set to 5 for better visualization. The implementation details and experimental settings can be found in Appendices C and D. |