MAPDP: Cooperative Multi-Agent Reinforcement Learning to Solve Pickup and Delivery Problems
Authors: Zefang Zong, Meng Zheng, Yong Li, Depeng Jin9980-9988
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on a randomly generated dataset and a real-world dataset. Experiments result shown that the proposed MAPDP outperforms all other baselines by at least 1.64% in all settings, and shows significant computation speed during solution inference. |
| Researcher Affiliation | Collaboration | 1Beijing National Research Center for Information Science and Technology 2Department of Electronic Engineering, Tsinghua University, Beijing, China 3Hitachi (China) Research & Development Corporation |
| Pseudocode | No | The paper does not contain a pseudocode block or an explicitly labeled algorithm. |
| Open Source Code | No | The paper does not contain any statement about making the source code available or provide a link to a code repository. |
| Open Datasets | No | We first generate a random dataset with randomly distributed node locations with demands for efficient performance comparison. ... We collect real-world data from an online logistic platform providing services in Guangdong, China, including more than 100 thousand order pairs within a month. |
| Dataset Splits | No | The paper describes using randomly generated and real-world datasets and mentions network training, but does not specify explicit train/validation/test splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | Yes | All experiments are conducted using Pytorch 1.7 on 4 2080Ti GPUs. |
| Software Dependencies | Yes | All experiments are conducted using Pytorch 1.7 on 4 2080Ti GPUs. |
| Experiment Setup | Yes | The networks are trained via Adam optimizer with L = 3, dk = 128, H = 8 and learning rate lr = 0.001. |