Summary of Garlic: Gpt-augmented Reinforcement Learning with Intelligent Control For Vehicle Dispatching, by Xiao Han et al.
GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching
by Xiao Han, Zijian Zhang, Xiangyu Zhao, Yuanshao Zhu, Guojiang Shen, Xiangjie Kong, Xuetao Wei, Liqiang Nie, Jieping Ye
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces GARLIC, a framework for vehicle dispatching in ride-hailing services, which addresses challenges such as unpredictable traffic conditions, diverse driver behaviors, and fluctuating supply and demand patterns. GARLIC utilizes multiview graphs to capture hierarchical traffic states, learns a dynamic reward function that accounts for individual driving behaviors, and integrates a GPT model trained with a custom loss function to optimize dispatching policies. The framework is evaluated on two real-world datasets, demonstrating its effectiveness in aligning with driver behaviors while reducing empty load rates of vehicles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GARLIC is a new way to help ride-hailing services get people where they need to go by making sure drivers have the right routes and passengers are matched with available rides. The problem is that current systems don’t account for things like traffic jams, driver personalities, or changes in demand. GARLIC tries to fix this by using special graphs to understand how traffic works and learning from what drives people’s behavior. It also uses a language model to make predictions and decide the best route for each ride. In real-world tests, GARLIC did a better job than other systems of matching drivers with passengers while keeping the roads clear. |
Keywords
» Artificial intelligence » Gpt » Language model » Loss function
