Multi-Agent Determinantal Q-Learning
Authors: Yaodong Yang, Ying Wen, Jun Wang, Liheng Chen, Kun Shao, David Mguni, Weinan Zhang
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm on various cooperative benchmarks; its effectiveness has been demonstrated when compared with the state-of-the-art. 4. Experiments We compare Q-DPP with state-of-the-art CTDE solvers for multi-agent cooperative tasks, including COMA (Foerster et al., 2018), VDN (Sunehag et al., 2017), QMIX (Rashid et al., 2018), QTRAN (Son et al., 2019), and MAVEN (Mahajan et al., 2019). |
| Researcher Affiliation | Collaboration | 1Huawei Technology R&D UK. 2University College London. 3Shanghai Jiaotong University. |
| Pseudocode | Yes | Algorithm 1 Multi-Agent Determinantal Q-Learning |
| Open Source Code | Yes | Code is released in https://github. com/QDPP-Git Hub/QDPP. |
| Open Datasets | Yes | We consider four cooperative tasks in Fig. 3, all of which require non-trivial value function decomposition to achieve the largest reward. All baselines are imported from Py MARL (Samvelyan et al., 2019). |
| Dataset Splits | No | The paper mentions training on 'sampled mini-batch of transition data' but does not specify any explicit train/validation/test dataset splits, percentages, or methodology for partitioning data for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for experiments, such as CPU or GPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions using PyMARL for baselines, but it does not provide specific version numbers for PyMARL or any other software dependencies needed to replicate the experiments. |
| Experiment Setup | Yes | Detailed settings are in Appendix B. |