Skip to main content
Skip to "About government"
Language selection
Français
Government of Canada /
Gouvernement du Canada
Search
Search the website
Search
Menu
Main
Menu
Jobs and the workplace
Immigration and citizenship
Travel and tourism
Business and industry
Benefits
Health
Taxes
Environment and natural resources
National security and defence
Culture, history and sport
Policing, justice and emergencies
Transport and infrastructure
Canada and the world
Money and finances
Science and innovation
You are here:
Canada.ca
Library and Archives Canada
Services
Services for galleries, libraries, archives and museums (GLAMs)
Theses Canada
Item – Theses Canada
Page Content
Item – Theses Canada
OCLC number
1334672550
Link(s) to full text
LAC copy
Author
Daniluk, Steven.
Title
An Advice Mechanism for Heterogeneous Robot Teams.
Degree
M.A.S. -- University of Toronto, 2017.
Publisher
[Toronto, Ontario] : University of Toronto, 2017
Description
1 online resource
Abstract
The use of reinforcement learning for robot teams has enabled complex tasks to be performed, but at the cost of requiring a large amount of exploration. Exchanging information between robots in the form of advice is one method to accelerate performance improvements. This thesis presents an advice mechanism for robot teams that utilizes advice from heterogeneous advisers via a method guaranteeing convergence to an optimal policy. The presented mechanism has the capability to use multiple advisers at each time step, and decide when advice should be requested and accepted, such that the use of advice decreases over time. Additionally, collective collaborative, and cooperative behavioural algorithms are integrated into a robot team architecture, to create a new framework that provides fault tolerance and modularity for robot teams.
Other link(s)
tspace.library.utoronto.ca
hdl.handle.net
Subject
Advice
Multi Agent Systems
Reinforcement Learning
Robot Teams
Date modified:
2022-09-01