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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


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

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

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

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

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

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

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

Conference: Road Safety & Simulation 2019

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