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..
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
Authors: Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results in different domains confirm TESSERACT s gains in sample efficiency predicted by the theory. Empirical results in different domains confirm TESSERACT s gains in sample efficiency predicted by the theory. |
| Researcher Affiliation | Collaboration | 1University of Oxford 2University College London 3NVIDIA. |
| Pseudocode | Yes | Algorithm 1 Model-based Tesseract; Algorithm 2 Model-free Tesseract |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is open-source or publicly available. |
| Open Datasets | Yes | Star Craft Multi-Agent Challenge (SMAC) (Samvelyan et al., 2019) |
| Dataset Splits | No | The paper mentions using the Star Craft Multi-Agent Challenge (SMAC) but does not provide specific details on training, validation, and test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The acknowledgements mention a "generous equipment grant from NVIDIA" but do not specify any concrete hardware details such as CPU/GPU models or memory specifications used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | No | The paper describes the methods and presents empirical results but does not include specific details about experimental setup such as hyperparameter values (e.g., learning rate, batch size) or other system-level training settings in the main text. |