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 [1].
MAVEN: Multi-Agent Variational Exploration
Authors: Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, Shimon Whiteson
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show that MAVEN achieves significant performance improvements on the challenging SMAC domain [43]. |
| Researcher Affiliation | Academia | Dept. of Computer Science, University of Oxford Russian-Armenian University |
| Pseudocode | Yes | Algorithm 1 MAVEN |
| Open Source Code | No | The paper does not provide any explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | Star Craft Multi-Agent Challenge We consider a challenging set of cooperative Star Craft II maps from the SMAC benchmark [43] which Samvelyan et al. have classified as Easy, Hard and Super Hard. |
| Dataset Splits | No | The paper mentions '32 evaluation episodes' and '100k training steps' but does not provide specific dataset split percentages, sample counts, or references to predefined train/validation/test splits. |
| Hardware Specification | No | The paper mentions 'generous equipment grant by NVIDIA and cloud credit grant from Oracle Cloud Innovation Accelerator' but does not provide specific GPU models, CPU models, or detailed cloud instance types used for experiments. |
| Software Dependencies | No | The paper does not list specific software components with their version numbers (e.g., Python 3.x, PyTorch x.x, CUDA x.x). |
| Experiment Setup | Yes | We use grid search to tune hyperparameters. Appendix C.1 contains additional experimental details. |