Path Planning using Reinforcement Learning and Objective Data

dc.contributor.authorXia, Tian
dc.contributor.authorHan, Zijian
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T14:58:43Z
dc.date.available2019-07-03T14:58:43Z
dc.date.issued2018
dc.description.abstractWith the rapid development of autonomous driving vehicles, decision making for path planning has become advanced and challenging topics. Traditional planning and control methods are usually limited by the difficulty to find good solutions, so deep machine learning has become engineers’ focus in order to solve these problems. Several related works using reinforcement learning have been done in the simulation environment TORCS. This thesis will focus on training an vehicle to learn driving at certain target speed on high way condition without collision. A complete learning structure is designed for vehicle system, and a hierarchical learning algorithm will be used with deep reinforcement learning methods. Deep Q learning is used to learn option level of policy, and deep deterministic policy gradient is used to learn primitive action level of policies. Neural networks are used to approximate the value functions. The training results are tested on various set up of opponents vehicles on the track, with the probability of damage recorded and compared.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/256451
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectComputer and Information Science
dc.titlePath Planning using Reinforcement Learning and Objective Data
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeSystems, control and mechatronics (MPSYS), MSc
Ladda ner
Original bundle
Visar 1 - 1 av 1
Hämtar...
Bild (thumbnail)
Namn:
256451.pdf
Storlek:
5.73 MB
Format:
Adobe Portable Document Format
Beskrivning:
Fulltext