Summary of Deep Reinforcement Learning For Real-time Ground Delay Program Revision and Corresponding Flight Delay Assignments, by Ke Liu et al.
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments
by Ke Liu, Fan Hu, Hui Lin, Xi Cheng, Jianan Chen, Jilin Song, Siyuan Feng, Gaofeng Su, Chen Zhu
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 explores the optimization of Ground Delay Programs (GDP) in Air Traffic Management (ATM) using Reinforcement Learning (RL). It develops two RL models: Behavioral Cloning (BC) and Conservative Q-Learning (CQL), designed to enhance GDP efficiency by integrating ground and airborne delays, terminal area congestion, and predicted uncertainties. The paper constructs a simulated environment, SAGDP_ENV, incorporating real operational data from Newark Liberty International Airport (EWR). The models’ performance is evaluated against actual operational data from 2019, revealing challenges in learning effectively due to oversimplified environmental assumptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers to help manage airport traffic. It creates two programs that can learn and make decisions based on real-world data. These programs aim to make better choices about how many planes can take off and land at an airport. The program uses a computer simulation to test its ideas and see if they work well in different situations. Even though the program is good, it still has some problems learning from the data. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
