Multiagent Decision Making For Maritime Traffic Management
Authors: Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau6171-6178
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on synthetic and real world problems show that our approach can significantly reduce congestion while keeping the traffic throughput high. |
| Researcher Affiliation | Collaboration | Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau School of Information Systems Singapore Management University {arambamjs.2016,dtnguyen.2014,akshatkumar,hclau}@smu.edu.sg ... This research is supported by the Agency for Science, Technology and Research (A*STAR), Fujitsu Limited and the National Research Foundation Singapore as part of the A*STAR-Fujitsu SMU Urban Computing and Engineering Centre of Excellence. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not state that the source code for their methodology is open-source or provide any link to it. |
| Open Datasets | No | The paper mentions using "4 months historical AIS (Automatic Identification System) data" but does not provide any link, citation, or access information for this dataset to be considered publicly available or open. |
| Dataset Splits | No | The paper refers to using "synthetic and real-world instances" and "4 months historical AIS data" but does not specify any training, validation, or test dataset splits (e.g., percentages, counts, or k-fold cross-validation). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processors, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions using baselines like "deep deterministic policy gradient (DDPG) (Lillicrap et al. 2015)" but does not specify any software names with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | A detailed description about all experimental setups (policy structure, and other settings) are provided in the appendix. ... resource penalty wr = 50 (after some trial-and-error this value worked best), delay penalty wd = 1. |