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.
Multi-Agent Determinantal Q-Learning
Authors: Yaodong Yang, Ying Wen, Jun Wang, Liheng Chen, Kun Shao, David Mguni, Weinan Zhang
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 18,698 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,638 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 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. |