How Knowledge about the Environment Influences the Performance of an Artificial Agent in a Multi-Agent Situation when trained using Reinforcement Learning
Keywords
No Thumbnail Available
Authors
Issue Date
2020-07-10
Language
en
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Robots are becoming increasingly popular in society, which increases the
importance of them being complex cooperative and competitive agents. This
study investigates whether knowledge about the environment improves the
performance of an artificial agent in a multi-agent situation when trained using
reinforcement learning. The influence of several types of knowledge has been
researched using a simulation created in Unity. The game Capture the Flag poses
as multi-agent situation, as it consists of multiple individual players whose
objective it is to capture the other team's flag and bring it back safely to their own
homebase. The agents are only rewarded for winning (and punished for losing),
and have been trained with the use of several training strategies such as
curriculum learning. Analysis of the results shows that knowledge has an
influence on the winning rate of these artificial agents. The best performance was
seen in teams of agents with access to knowledge about which team possesses
which flags in combination with an extended visual range, though not all
information turned out to be equally valuable. Still, all of the teams with added
knowledge outperformed the teams without any added knowledge, and in most of
them several distinct cooperative and competitive strategies have automatically
emerged. This concludes that more knowledge improves the performance of an
artificial agent in a multi-agent situation when trained using a reinforcement
learning algorithm.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen