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.
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model
Authors: Gen Li, Yuejie Chi, Yuting Wei, Yuxin Chen
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 21,154 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,196 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a theoretical work that we do not foresee any potential negative societal impacts. |
| Researcher Affiliation | Academia | Gen Li UPenn Yuejie Chi CMU Yuting Wei UPenn Yuxin Chen UPenn |
| Pseudocode | Yes | Algorithm 1: Q-FTRL. Algorithm 2: Auxiliary function sampling i, h, πh = {πj,h}j [m] . |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets. Thus, no training dataset information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets. Thus, no dataset split information for validation is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on experimental hardware. The ethics statement indicates N/A for compute resources. |
| Software Dependencies | No | The paper is theoretical and does not report on software dependencies with specific version numbers for experimental reproducibility. The ethics statement indicates N/A for experimental details. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameters or training configurations. The ethics statement indicates N/A for training details. |