Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
Authors: Shariq Iqbal, Christian A Schroeder De Witt, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our approach, Randomized Entity-wise Factorization for Imagined Learning (REFIL), outperforms all strong baselines by a significant margin in challenging multi-task Star Craft micromanagement settings. In our experiments, we aim to answer the following questions: |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of Southern California 2Department of Computer Science, University of Oxford 3Department of Software Technology, Delft University of Technology 4Google Research. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at: https://github.com/shariqiqbal2810/REFIL |
| Open Datasets | Yes | We next test on the Star Craft multi-agent challenge (SMAC) (Samvelyan et al., 2019). |
| Dataset Splits | No | The paper references training and testing but does not provide specific details on validation splits or exact percentages for data partitioning. |
| Hardware Specification | No | The paper mentions an 'equipment grant from NVIDIA' but does not specify exact hardware details such as GPU/CPU models or memory used for experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | No | The paper describes general model architecture and training procedures but does not explicitly provide concrete hyperparameter values or detailed system-level training settings in the main text. |