Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Venue PDF | LLM Run Details | Input Tokens: 19,221 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,189 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |