Particle Swarm Based Reinforcement Learning for Path Planning and Traffic Congestion

Show simple item record

dc.contributor Vrbsky, Susan
dc.contributor Yoon, Hwan-Sik
dc.contributor Hong, Xiaoyan
dc.contributor Smith, Randy
dc.contributor.advisor Atkison, Travis Phan, Ashley 2022-09-28T14:54:42Z 2022-09-28T14:54:42Z 2022
dc.identifier.other u0015_0000001_0004426
dc.identifier.other Phan_alatus_0004D_14873
dc.description Electronic Thesis or Dissertation
dc.description.abstract In 2019, the average American commuter wasted approximately two and a half days due to traffic delays. Researchers suggest that these delays could be relieved by the addition of intelligent transportation systems, such as navigational systems that identify multiple high-speed travel routes or sophisticated traffic signals that can adapt to different traffic patterns. This dissertation explores the hybridization of the swarm intelligence algorithm, particle swarm optimization, with the reinforcement learning algorithm, Q-learning, and the hierarchical reinforcement learning algorithm,MAX-Q, to produce an intelligent path-planning algorithm and an adaptive traffic control system. By combining these algorithms with particle swarm optimization, the search space of a single agent is reduced through the parallelization and collaboration of multiple agents. Alternatively, the use of a look-up table improves the performance of particle swarm optimization by enhancing the swarm's ability to learn and balance the local and global search. In order to further improve the performance of the hybrid algorithms, a local particle swarm optimization variant was incorporated into the algorithms' action selection policies. This combination results in two hybrid intelligent optimization algorithms, Q-learning with Local Particle Swarm Optimization and MAXQ with Particle Swarm Optimization. When tasked with path planning in the Taxi World environment, QLPSO and MAXQPSO collectively learned the optimal policy in 46.44% fewer episodes than state-of-the-art algorithms and completed the task in 25.57% fewer steps. Given the success of the novel methods in the path planning problem, the two algorithms were slightly modified to identify the optimal policies for the traffic control problem. For various traffic networks, the algorithms collectively minimized the total wait time by an average of 16.31% and decreased the average wait time per vehicle by 11.43%. The combination of PSO and the learning algorithms demonstrate notable benefits as intelligent transportation systems.
dc.format.medium electronic
dc.format.mimetype application/pdf
dc.language English
dc.language.iso en_US
dc.publisher University of Alabama Libraries
dc.relation.ispartof The University of Alabama Electronic Theses and Dissertations
dc.relation.ispartof The University of Alabama Libraries Digital Collections
dc.relation.hasversion born digital
dc.rights All rights reserved by the author unless otherwise indicated.
dc.title Particle Swarm Based Reinforcement Learning for Path Planning and Traffic Congestion
dc.type thesis
dc.type text University of Alabama. Department of Computer Science Computer science The University of Alabama doctoral Ph.D.

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


My Account