Reinforcement
Learning
Reinforcement learning (RL) is an area of machine learning that focuses on taking suitable action to maximize rewards, given a particular situation. It is employed by various software and machines to find the best possible behavior or path to take in a specific situation. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. The main elements of an RL system are the agent or the learner, the environment the agent interacts with, the policy that the agent follows to take actions, and the reward signal that the agent observes upon taking actions.
MEDIRL: Predicting The Visual Attention of Drivers Via Maximum Entropy Deep Inverse Reinforcement Learning
Sonia Baee • Erfan Pakdamanian • Inki Kim • Lu Feng • Vicente Ordonez • Laura Barnes
Jan 1, 2021
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ArXiv
Ramp Metering For A Distant Downstream Bottleneck Using Reinforcement Learning With Value Function Approximation
Yue Zhou • Kaan Ozbay • Pushkin Kachroo • Fan Zuo
Oct 28, 2020
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Journal Of Advanced Transportation
Intelligent Energy Management Systems For Electrified Vehicles: Current Status, Challenges, And Emerging Trends
Reihaneh Ostadian • John Ramoul • Atriya Biswas • Ali Emadi
Aug 20, 2020
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IEEE Open Journal of Vehicular Technology
Proximal Policy Optimization Through A Deep Reinforcement Learning Framework For Multiple Autonomous Vehicles At A Non-Signalized Intersection
Duy Quang Tran • Sang-Hoon Bae
Aug 18, 2020
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Applied Sciences
Collaborative Computing In Vehicular Networks: A Deep Reinforcement Learning Approach
Mushu Li • Jie Gao • Ning Zhang • Lian Zhao • Xuemin Shen
Jul 27, 2020
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ICC 2020 - 2020 IEEE International Conference on Communications
Multi-Agent Graph Reinforcement Learning For Connected Automated Driving
Jiawei Wang • Tiyanyu Shi • Yuankai Wu • Luis Miranda-Moreno
Jul 1, 2020
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Conference: ICML Workshop on AI for Autonomous Driving
Deep Truck: A deep Neural Network Model For Longitudinal Dynamics Of Heavy Duty Trucks
Sahleh Albeaik • Fang-Chieh Chou • Xiao-Yun Lu • Alexandre M. Bayen
Nov 28, 2019
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2019 IEEE Intelligent Transportation Systems Conference
Microscopic Simulation Based Study Of Pedestrian Safety Applications At Signalized Urban Crossings In A Connected-Automated Vehicle Environment And Reinforcement Learning Based Optimization Of Vehicle Decisions
Fan Zuo • Kaan Ozbay • Abdullah Kurkcu • Jingqin Gao • Hong Yang • Kun Xie
Oct 1, 2019
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Conference: Road Safety & Simulation 2019