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..
Settling the Variance of Multi-Agent Policy Gradients
Authors: Jakub Grudzien Kuba, Muning Wen, Linghui Meng, shangding gu, Haifeng Zhang, David Mguni, Jun Wang, Yaodong Yang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On benchmarks of Multi-Agent Mu Jo Co and Star Craft challenges, our OB technique effectively stabilises training and improves the performance of multi-agent PPO and COMA algorithms by a significant margin. Code is released at https://github.com/morning9393/ Optimal-Baseline-for-Multi-agent-Policy-Gradients. |
| Researcher Affiliation | Collaboration | 1Imperial College London, 2Huawei R&D UK, 3Shanghai Jiao Tong University, 4Institute of Automation, Chinese Academy of Science, 5University College London, 6Institute for AI, Peking University. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is released at https://github.com/morning9393/ Optimal-Baseline-for-Multi-agent-Policy-Gradients. |
| Open Datasets | Yes | Star Craft Multi-Agent Challenge (SMAC) [25]. In SMAC, each individual unit is controlled by a learning agent, which has finitely many possible actions to take. The units cooperate to defeat enemy bots across scenarios of different levels of difficulty. (...) Multi-Agent Mu Jo Co [5]. |
| Dataset Splits | No | The paper does not explicitly provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'PyTorch' in Appendix D but does not specify version numbers for PyTorch or any other software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | For each of the baseline on each task, we report the results of five random seeds. We refer to Appendix F for the detailed hyper-parameter settings for baselines. |