Show simple item record

dc.contributor.authorHarmon, Dain
dc.date.accessioned2021-03-04T21:48:37Z
dc.date.available2021-03-04T21:48:37Z
dc.date.issued2020-12
dc.identifier.urihttp://hdl.handle.net/11122/11902
dc.descriptionMaster's Project (M.S.) University of Alaska Fairbanks, 2020en_US
dc.description.abstractWe consider the effects of varying individualistic and team rewards on learning for a Deep Q-Network AI in a multi-agent system, using a synthetic team game ‘Futlol’ designed for this purpose. Experimental results with this game using the OpenSpiel framework indicate that mixed reward structures result in lower win rates. It is unclear if this is due to faster learning on simpler reward structures or a flaw in the nature of the reward system.en_US
dc.language.isoen_USen_US
dc.titleMeasuring the impact of cooperative rewards on AIen_US
dc.typeOtheren_US
dc.type.degreemsen_US
dc.identifier.departmentDepartment of Computer Scienceen_US
dc.contributor.chairLawlor, Orion S.
dc.contributor.committeeChappell, Glenn G.
dc.contributor.committeeMetzgar, Jonathan B.
refterms.dateFOA2021-03-04T21:48:37Z


Files in this item

Thumbnail
Name:
Harmon_D_2020.pdf
Size:
9.241Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record