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.
I2Q: A Fully Decentralized Q-Learning Algorithm
Authors: Jiechuan Jiang, Zongqing Lu
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,638 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,268 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 | Empirically, we show that I2Q can achieve remarkable improvement in a variety of cooperative multi-agent tasks. |
| Researcher Affiliation | Academia | Jiechuan Jiang School of Computer Science Peking University EMAIL Zongqing Lu School of Computer Science Peking University EMAIL |
| Pseudocode | Yes | Algorithm 1. I2Q for each agent i |
| Open Source Code | Yes | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | Multi-Agent Mu Jo Co [18] |
| Dataset Splits | No | The main paper text states 'More details about hyperparameters are available in Appendix C.' but does not explicitly provide training/validation/test dataset splits within the provided text. |
| Hardware Specification | No | The main paper text states 'We include the details of computation in Appendix C.' but does not explicitly provide specific hardware details such as GPU models or processor types within the provided text. |
| Software Dependencies | No | The paper mentions 'We adopt the implementation of Py MARL [23]' but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | More details about hyperparameters are available in Appendix C. |