Deep Multi-Agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic-Reference-Cited by-同舟云学术

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Deep Multi-Agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic

Published:2023-11 Issue:11 Volume:24 Page:11623-11638
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Chen Dong¹^ORCID,Hajidavalloo Mohammad R.¹^ORCID,Li Zhaojian¹^ORCID,Chen Kaian¹^ORCID,Wang Yongqiang²^ORCID,Jiang Longsheng³^ORCID,Wang Yue³^ORCID

Affiliation:

1. Department of Mechanical Engineering, Michigan State University, Lansing, MI, USA

2. Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA

3. Department of Mechanical Engineering, Clemson University, Clemson, SC, USA

Funder

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

https://ieeexplore.ieee.org/ielam/6979/10304348/10159552-aam.pdf

Reference71 articles.

1. Cooperative vehicle path generation during merging using model predictive control with real-time optimization

2. On local rewards and scaling distributed reinforcement learning;bagnell;Proc Adv Neural Inf Process Syst,2005

3. Anti-Jerk On-Ramp Merging Using Deep Reinforcement Learning

4. Review of road traffic control strategies

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