Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
Authors: Siddharth Nayak, Kenneth Choi, Wenqi Ding, Sydney Dolan, Karthik Gopalakrishnan, Hamsa Balakrishnan
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed model on four different navigation tasks by modifying the MAPE (Lowe et al., 2017b). Figure 3 shows the training performance of Infor MARL and the best-performing of the baselines mentioned above for the target task environment. |
| Researcher Affiliation | Academia | 1Massachusetts Institute of Technology, Cambridge, USA 2Stanford University, Stanford, USA. |
| Pseudocode | No | No pseudocode or algorithm blocks are present. |
| Open Source Code | Yes | Code available at https://github.com/nsidn98/Infor MARL. |
| Open Datasets | Yes | We evaluate our proposed model on four different navigation tasks by modifying the MAPE (Lowe et al., 2017b). |
| Dataset Splits | No | The paper discusses training and testing performance, but does not explicitly detail specific train/validation/test dataset splits with percentages or counts for reproducibility. |
| Hardware Specification | No | The authors would like to thank the MIT Super Cloud (Reuther et al., 2018) and the Lincoln Laboratory Supercomputing Center for providing high performance computing resources that have contributed to the research results reported within this paper. |
| Software Dependencies | No | We implemented Infor MARL by modifying the official codebase for MAPPO in Py Torch. (No version specified for PyTorch or any other library). |
| Experiment Setup | Yes | Tables 4, 5, 6, 7 show the hyperparameters for Infor MARL, MAPPO, MADDPG, MATD3, QMIX and VDN. |