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 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |