Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning
Authors: Chenghao Li, Tonghan Wang, Chengjie Wu, Qianchuan Zhao, Jun Yang, Chongjie Zhang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that our method achieves state-of-the-art performance on Google Research Football and super hard Star Craft II micromanagement tasks. We benchmark our approach on Google Research Football (GRF) [18], and Star Craft II micromanagement tasks (SMAC) [16]. We compare our approach against multi-agent value-based methods (QMIX [5], QPLEX [6]), variational exploration (MAVEN [25]), and individuality emergence (EOI [26]) methods. We carry out ablation studies to test the contribution of its three main components. |
| Researcher Affiliation | Academia | Chenghao Li, Tonghan Wang, Chengjie Wu, Qianchuan Zhao, Jun Yang , Chongjie Zhang Tsinghua University EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Videos are available at https://sites.google.com/view/celebrate-diversity-shared with codes. |
| Open Datasets | Yes | We benchmark our approach on Google Research Football (GRF) [18], and Star Craft II micromanagement tasks (SMAC) [16]. |
| Dataset Splits | No | The paper discusses training and performance evaluation but does not specify explicit train/validation/test dataset splits (percentages, counts, or predefined splits) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions various algorithms and frameworks (e.g., QPLEX, QMIX), but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper mentions hyperparameters like β and λ but does not provide their specific values or other concrete experimental setup details such as learning rates, batch sizes, or optimizer settings in the main text. |