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.
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