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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning
Authors: Jianzhun Shao, Yun Qu, Chen Chen, Hongchang Zhang, Xiangyang Ji
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further conduct experiments on four environments including both discrete and continuous action settings on both existing and our man-made datasets, demonstrating that CFCQL outperforms existing methods on most datasets and even with a remarkable margin on some of them. |
| Researcher Affiliation | Academia | Jianzhun Shao , Yun Qu , Chen Chen, Hongchang Zhang, Xiangyang Ji Department of Automation Tsinghua University, Beijing, China EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 CFCQL-D and CFCQL-C |
| Open Source Code | Yes | Our code and datasets are available at: https://github.com/thu-rllab/CFCQL |
| Open Datasets | Yes | With datasets collected by Pan et al. [34] and ourselves, our method outperforms existing methods in most settings and even with a large margin on some of them. and Our code and datasets are available at: https://github.com/thu-rllab/CFCQL |
| Dataset Splits | No | The paper describes how datasets were collected (e.g., 'The datasets are made based on the training process or trained model of QMIX[37]') but does not explicitly state train/validation/test splits by percentages, absolute counts, or by referencing predefined standard splits for their experiments. |
| Hardware Specification | Yes | We use 2 servers to run all the experiments. Each one has 8*NVIDIA RTX 3090 GPUs, and 2*AMD 7H12 CPUs. Each setting is repeated for 5 seeds. |
| Software Dependencies | No | The paper refers to using various open-source implementations (e.g., 'from Lowe et al. [27]', 'from Samvelyan et al. [40]') and general tools like 'Q-learning' or 'TD3', but does not provide specific version numbers for software dependencies or libraries (e.g., 'Python 3.8', 'PyTorch 1.9'). |
| Experiment Setup | Yes | Please refer to this repository12 for the code, datasets and the hyper-parameters of our method. |