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dc.creatorCrouther, Paul
dc.date.accessioned2018-07-24T15:32:26Z
dc.date.available2019-05-01T06:10:06Z
dc.date.created2017-05
dc.date.issued2015-09-23
dc.date.submittedMay 2017
dc.identifier.urihttp://hdl.handle.net/1969.1/167883
dc.description.abstractAutonomous travel poses challenges in machine learning navigation. Different approaches have been considered, such as reinforcement learning, dynamic programming methodologies, and other artificial intelligence assisted solutions. Dynamic programming methodologies exist to assist an agent, or robot, to interact with the environment based on predetermined environments. Real-world terrain and real-time complexity make learning and interacting with an environment particularly difficult. Reinforcement learning is a methodology that provides the agent with a learning system that is more environment and terrain independent. To this end, reinforcement learning is an effective way to solve the autonomous vehicle problem by proposing a model-free, or nearly model-free approach. The reinforcement learning algorithm used for this autonomous vehicle is Q-learning, which is an unsupervised learning algorithm where the agent explores from state to state until his goal is reached. With this algorithm, learning is achieved by sequential states of winning and losing, subject to obtaining the goal or failing to obtain the goal. The objective of this approach is to use particular goals to obtain navigation independent of the environment.en
dc.format.mimetypeapplication/pdf
dc.subjectreinforcement learningen
dc.subjectroboticsen
dc.subjectmarkoven
dc.subjectcomputer scienceen
dc.subjectcomputer engineeringen
dc.subjectelectrical engineeringen
dc.subjectoptimizationen
dc.subjectmachine learningen
dc.subjectautonomousen
dc.subjectvehiclesen
dc.subjectroboten
dc.subjectcaren
dc.subjectartificial intelligenceen
dc.subjectneural networksen
dc.subjectdynamic programmingen
dc.subjectQ-learningen
dc.subjectmodel-freeen
dc.subjectagenten
dc.titleReinforcement Learning For Collision Avoidanceen
dc.typeThesisen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorUndergraduate Research Scholars Programen
dc.contributor.committeeMemberLi, Peng
dc.type.materialtexten
dc.date.updated2018-07-24T15:32:26Z
local.embargo.terms2019-05-01


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