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 | Conference PDF | Archive PDF | Plain Text | 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. |