The Emergence of Individuality

Authors: Jiechuan Jiang, Zongqing Lu

ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Empirically, we show that EOI outperforms existing methods in a variety of multi-agent cooperative scenarios.
Researcher Affiliation Academia Jiechuan Jiang 1 Zongqing Lu 1 1Peking University. Correspondence to: Zongqing Lu <zongqing.lu@pku.edu.cn>.
Pseudocode No The paper describes the proposed method in narrative text and includes architectural diagrams, but it does not contain structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any concrete access information (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described.
Open Datasets No The paper describes several custom or adapted environments (Pac-Men, Windy Maze, Firefighters, Battle, 10 vs 10) but does not provide specific access information (links, DOIs, repositories, or formal citations to public datasets) for the data used or generated within these environments.
Dataset Splits No The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing.
Hardware Specification No The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments.
Software Dependencies No The paper mentions several algorithms and environments but does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment.
Experiment Setup No The paper states 'The details about the experimental settings and the hyperparameters are available in Appendix B.', indicating that these details are not provided in the main text.