A Multi-Agent Reinforcement Learning Approach for Safe and Efficient Behavior Planning of Connected Autonomous Vehicles-Reference-Cited by-同舟云学术

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A Multi-Agent Reinforcement Learning Approach for Safe and Efficient Behavior Planning of Connected Autonomous Vehicles

Published:2023 Issue: Volume: Page:1-17
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Han Songyang¹^ORCID,Zhou Shanglin¹^ORCID,Wang Jiangwei²^ORCID,Pepin Lynn¹,Ding Caiwen¹^ORCID,Fu Jie³^ORCID,Miao Fei¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, University of Connecticut, Storrs, Mansfield, CT, USA

2. Department of Electrical and Computer Engineering, University of Connecticut, Storrs, Mansfield, CT, USA

3. Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA

Funder

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/4358928/10367764.pdf?arnumber=10367764

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1. Deep Reinforcement Learning-Driven Collaborative Rounding-Up for Multiple Unmanned Aerial Vehicles in Obstacle Environments;Drones;2024-09-06

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3. A CNN-SAM-LSTM hybrid neural network for multi-state estimation of lithium-ion batteries under dynamical operating conditions;Energy;2024-05

4. Q-Learning Methods for LQR Control of Completely Unknown Discrete-Time Linear Systems;IEEE Transactions on Automation Science and Engineering;2024

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