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.
Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts
Authors: Weinan Zhang, Xihuai Wang, Jian Shen, Ming Zhou
IJCAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 16,400 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,839 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 | Empirical experiments on competitive and cooperative tasks demonstrate that AORPO can achieve improved sample ef๏ฌciency with comparable asymptotic performance over the compared MARL methods. |
| Researcher Affiliation | Academia | Weinan Zhang , Xihuai Wang , Jian Shen , Ming Zhou Shanghai Jiao Tong University EMAIL |
| Pseudocode | Yes | Algorithm 1: AORPO Algorithm |
| Open Source Code | No | No explicit statement providing concrete access to source code (e.g., repository link, explicit code release statement) for the methodology described in this paper was found. |
| Open Datasets | Yes | Based on a multi-agent particle environment, we evaluate our method in two types of cooperative tasks... Multi-Agent Particle Environment [Lowe et al., 2017] |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning into train/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions algorithms like MASAC and MADDPG but does not provide specific software names with version numbers (e.g., Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | Other implementation details, including network architectures and important hyperparameters, are provided in Appendix F. |