GPLight: Grouped Multi-agent Reinforcement Learning for Large-scale Traffic Signal Control
Authors: Yilin Liu, Guiyang Luo, Quan Yuan, Jinglin Li, Lei Jin, Bo Chen, Rui Pan
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To verify the effectiveness of our method, we conduct experiments on both synthetic and real-world datasets, with up to 1,089 intersections. Compared with state-of-the-art methods, experiment results demonstrate the superiority of our proposed method, especially in large-scale CTL. |
| Researcher Affiliation | Academia | State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China 2State Key Laboratory of Integrated Services Networks, Xidian University, Xi an 710126, China 3School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China {liuyilin10, luoguiyang, yuanquan, jlli, jinlei, Czb199871, panrui805}@bupt.edu.cn |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | No explicit statement or link to open-source code for the described methodology was found. |
| Open Datasets | Yes | We run our experiments on City Flow [Zhang et al., 2019], a traffic simulator. In the synthetic dataset, we will use two kinds of maps. They are made up of different numbers of intersections. Synthetic maps are generated via Cityflow and include road attributes such as the number of lanes and road speed limits. We also experiment with real traffic data. For the convenience of subsequent comparative experiments, we continue to use the real maps of Hangzhou, Jinan in China, and New York in the USA. Their road network structure can be imported from Open Street Map, as shown in Figure 4. |
| Dataset Splits | No | The paper mentions using synthetic and real-world datasets for experiments, but does not explicitly describe training, validation, and test splits with specific percentages or counts. |
| Hardware Specification | No | No specific hardware (e.g., CPU, GPU models, memory, or cloud instance types) used for running the experiments was explicitly described. |
| Software Dependencies | No | The paper mentions software like City Flow and SUMO, but does not provide specific version numbers for these or any other software dependencies, libraries, or programming languages used. |
| Experiment Setup | No | The paper describes settings for the simulation environment, such as car parameters and signal timings ('Each car has its own set of parameters, e.g., acceleration, maximum speed', 'each green signal is followed by three seconds of yellow light and two-second all red time'). However, it does not provide specific hyperparameters or system-level training settings for the deep reinforcement learning model (e.g., learning rate, batch size, optimizer details). |