RAMRL: Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning-Reference-Cited by-同舟云学术

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RAMRL: Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning

Published:2023-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)
language:
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Author:

Bagwe Gaurav¹,Yuan Xiaoyong²,Chen Xianhao³,Zhang Lan¹

Affiliation:

1. Michigan Technological University,Department of Electrical and Computer Engineering,Houghton,MI,USA

2. Michigan Technological University,College of Computing,Houghton,MI,USA

3. University of Hong Kong,Department of Electrical and Electronic Engineering,Hong Kong,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10210727/10210735/10211028.pdf?arnumber=10211028

Reference45 articles.

1. Traffic safety facts,2015

2. A Machine Learning-Based Defensive Alerting System Against Reckless Driving in Vehicular Networks

3. Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey

4. Proximal policy optimization algorithms;Schulman,2017

5. Learning Highway Ramp Merging Via Reinforcement Learning with Temporally-Extended Actions

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. Platoon-based control strategy in mixed traffic to improve ramp merging efficiency and fuel reduction;Physica A: Statistical Mechanics and its Applications;2024-07

2. Trustworthy autonomous driving via defense-aware robust reinforcement learning against worst-case observational perturbations;Transportation Research Part C: Emerging Technologies;2024-06

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