Optimistic Value Instructors for Cooperative Multi-Agent Reinforcement Learning
Authors: Chao Li, Yupeng Zhang, Jianqi Wang, Yujing Hu, Shaokang Dong, Wenbin Li, Tangjie Lv, Changjie Fan, Yang Gao
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluation of OVI on various cooperative multi-agent tasks demonstrates its superior performance against multiple baselines, highlighting its effectiveness. |
| Researcher Affiliation | Collaboration | Chao Li1, Yupeng Zhang2, Jianqi Wang3, Yujing Hu4 , Shaokang Dong1, Wenbin Li1, Tangjie Lv4, Changjie Fan4, Yang Gao1* 1 State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 2 Alibaba DAMO Academy, Hangzhou, China 3 Meituan, Beijing, China 4 Net Ease Fuxi AI Lab, Hangzhou, China |
| Pseudocode | No | The paper describes the algorithm in text and equations, but does not provide a formal pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology, nor does it include a direct link to a code repository. |
| Open Datasets | Yes | We then evaluate the performance of our algorithm by comparing it against multiple baselines in a variety of cooperative multi-agent tasks, which include matrix game, predator and prey, and Star Craft Multi-Agent Challenge (Samvelyan et al. 2019). |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or explicit cross-validation setup). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the implementation. |
| Experiment Setup | No | The paper mentions that 'More details about algorithmic implementations and experimental settings are provided in Appendix B,' but does not include specific hyperparameter values or training configurations in the main body. |