Multi-Agent Based Transfer Learning for Data-Driven Air Traffic Applications

2024-01-23 · Chuhao Deng, Hong-Cheol Choi, Hyunsang Park, Inseok Hwang

Research in developing data-driven models for Air Traffic Management (ATM) has gained a tremendous interest in recent years. However, data-driven models are known to have long training time and require large datasets to achieve good performance. To address the two issues, this paper proposes a Multi-Agent Bidirectional Encoder Representations from Transformers (MA-BERT) model that fully considers the multi-agent characteristic of the ATM system and learns air traffic controllers' decisions, and a pre-training and fine-tuning transfer learning framework. By pre-training the MA-BERT on a large dataset from a major airport and then fine-tuning it to other airports and specific air traffic applications, a large amount of the total training time can be saved. In addition, for newly adopted procedures and constructed airports where no historical data is available, this paper shows that the pre-trained MA-BERT can achieve high performance by updating regularly with little data. The proposed transfer learning framework and MA-BERT are tested with the automatic dependent surveillance-broadcast data recorded in 3 airports in South Korea in 2019.

📄 PDF Abstract BibTeX arXiv:2401.14421

Code (0)

등록된 구현이 없습니다.

Tasks

Management Transfer Learning

Transfer Learning versus Multi-agent Learning regarding Distributed Decision-Making in Highway Traffic

2018-10-19 · Mark Schutera, Niklas Goby, Dirk Neumann, Markus Reischl

Transportation and traffic are currently undergoing a rapid increase in terms of both scale and complexity. At the same time, an increasing share of traffic participants are being transformed into agents driven or suppor…

Decision Making Deep Reinforcement Learning Reinforcement Learning Transfer Learning

Scalable Multiagent Driving Policies For Reducing Traffic Congestion

2021-02-26 · Jiaxun Cui, William Macke, Harel Yedidsion, Daniel Urieli 외

Traffic congestion is a major challenge in modern urban settings. The industry-wide development of autonomous and automated vehicles (AVs) motivates the question of how can AVs contribute to congestion reduction. Past re…

Transfer Learning Transfer Reinforcement Learning

X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner

2024-04-18 · Haoyuan Jiang, Ziyue Li, Hua Wei, Xuantang Xiong 외

The effectiveness of traffic light control has been significantly improved by current reinforcement learning-based approaches via better cooperation among multiple traffic lights. However, a persisting issue remains: how…

Traffic Signal Control

TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction

2023-03-07 · Zhejun Zhang, Alexander Liniger, Dengxin Dai, Fisher Yu 외

Data-driven simulation has become a favorable way to train and test autonomous driving algorithms. The idea of replacing the actual environment with a learned simulator has also been explored in model-based reinforcement…

Autonomous Driving Autonomous Vehicles Model-based Reinforcement Learning motion prediction

Guided Conditional Diffusion for Controllable Traffic Simulation

2022-10-31 · Ziyuan Zhong, Davis Rempe, Danfei Xu, Yuxiao Chen 외

Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles. Typical heuristic-based traffic models offer flexible control to make vehicles follow specific trajectories and …

Autonomous Vehicles Collision Avoidance