Graduation Year
2022
Document Type
Thesis
Degree
M.S.C.S.
Degree Name
MS in Computer Science (M.S.C.S.)
Degree Granting Department
Computer Science and Engineering
Major Professor
Alfredo Weitzenfeld, Ph.D.
Committee Member
Alessio Gaspar, Ph.D.
Committee Member
Zachariah Beasley, Ph.D.
Keywords
Deep Deterministic Policy Gradient, grSim, Reinforcement Learning, SSL
Abstract
In robotics soccer, decision-making is critical to the performance of a team’s SoftwareSystem. The University of South Florida’s (USF) RoboBulls team implements behavior for the robots by using traditional methods such as analytical geometry to path plan and determine whether an action should be taken. In recent works, Machine Learning (ML) and Reinforcement Learning (RL) techniques have been used to calculate the probability of success for a pass or goal, and even train models for performing low-level skills such as traveling towards a ball and shooting it towards the goal[1, 2]. Open-source frameworks have been created for training Reinforcement Learning models with the purpose of expanding upon existing research and allowing for further applications of RL to robot soccer[3]. This thesis aims to use these frameworks to supplement the existing publicly available resources, as well as to investigate whether implementing trained Neural Network (NN) models can improve the performance or quality of the existing USF RoboBulls software system.
Scholar Commons Citation
Rodney, Justin, "Analyzing Decision-making in Robot Soccer for Attacking Behaviors" (2022). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/9448
