Towards a Very Large Scale Traffic Simulator for Multi-Agent Reinforcement Learning Testbeds-Reference-Cited by-同舟云学术

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Towards a Very Large Scale Traffic Simulator for Multi-Agent Reinforcement Learning Testbeds

Published:2022-10-08 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
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Author:

Hu Zijian¹,Zhuge Chengxiang²,Ma Wei³

Affiliation:

1. The Hong Kong Polytechnic University,Department of Civil and Environmental Engineering,Kowloon,Hong Kong SAR

2. The Hong Kong Polytechnic University,Department of Land Surveying and Geo-Informatics,Kowloon,Hong Kong SAR

3. The Hong Kong Polytechnic University,Department of Civil and Environmental Engineering,Hong Kong SAR,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9921415/9921739/09921887.pdf?arnumber=9921887

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Demonstration-guided deep reinforcement learning for coordinated ramp metering and perimeter control in large scale networks;Transportation Research Part C: Emerging Technologies;2024-02

2. Applicability of the Future State Maximization Paradigm to Agent-Based Modeling: A Case Study on the Emergence of Socially Sub-Optimal Mobility Behavior;Systems;2023-02-14

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