Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Learning in Nonzero-Sum Stochastic Games with Potentials
Authors: David H Mguni, Yutong Wu, Yali Du, Yaodong Yang, Ziyi Wang, Minne Li, Ying Wen, Joel Jennings, Jun Wang
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 20,758 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,856 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 | We evaluate SPot-AC in three popular multi-agent environments: the particle world (Lowe et al., 2017), a network routing game (Roughgarden, 2007) and a Cournot duopoly problem (Agliari et al., 2016). |
| Researcher Affiliation | Collaboration | 1Huawei R&D UK 2Institute of Automation, Chinese Academy of Sciences 3University College London, UK 4Shanghai Jiao Tong University. |
| Pseudocode | Yes | Algorithm 1 SPot Q: Stochastic POTential Q-Learning; Algorithm 2 SPot-AC: Stochastic POTential Actor-Critic |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the release of source code for the described methodology. |
| Open Datasets | Yes | We evaluate SPot-AC in three popular multi-agent environments: the particle world (Lowe et al., 2017), a network routing game (Roughgarden, 2007) and a Cournot duopoly problem (Agliari et al., 2016). |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits. It only states that experiments are repeated for 5 independent runs. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper states "Further details on the settings can be found in Appendix" but does not provide specific experimental setup details or hyperparameters in the main text. |