TERL: Two-Stage Ensemble Reinforcement Learning Paradigm for Large-Scale Decentralized Decision Making in Transportation Simulation-Reference-Cited by-同舟云学术

TERL: Two-Stage Ensemble Reinforcement Learning Paradigm for Large-Scale Decentralized Decision Making in Transportation Simulation

Published:2023-12-01 Issue:12 Volume:35 Page:13043-13054
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Gu Ziyuan¹^ORCID,Yang Xun¹^ORCID,Zhang Qi¹^ORCID,Yu Wenwu²^ORCID,Liu Zhiyuan¹^ORCID

Affiliation:

1. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing, Jiangsu, China

2. School of Mathematics, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing, Jiangsu, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

High-Level Personnel Project of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10311056/10129851.pdf?arnumber=10129851

Reference43 articles.

1. The k-means Algorithm: A Comprehensive Survey and Performance Evaluation

2. Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving

3. Assessment of drivers' comprehension of traffic signs based on their traffic, personal and social characteristics

4. Urban Traffic Light Control via Active Multi-agent Communication and Supply-Demand Modeling

5. Multi-Task Travel Route Planning With a Flexible Deep Learning Framework

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