Show simple item record

dc.contributorAnsótegui Gil, Carlos José
dc.contributorUniversitat de Lleida. Escola Politècnica Superior
dc.contributor.authorMerino Pulido, Albert Eduard
dc.date.accessioned2018-10-03T08:25:42Z
dc.date.available2018-10-03T08:25:42Z
dc.date.issued2018-09
dc.identifier.urihttp://hdl.handle.net/10459.1/64813
dc.description.abstractRecently, there have been several advances on integrating Deep Neural Networks (DNNs) and Reinforcement Learning (RL) algorithms. These efforts led to the development of Deep Q-Learning (DQL) algorithms which have been applied successfully to develop competitive approaches for multiagent games. Both DNNs and RL algorithms are highly parameterized and di erent settings can have a dramatic impact on their e ciency. Thus, DQL algorithms can also greatly bene t from a good setting of their parameters. In this project, we show how to apply Automatic Con guration (AC) tools in order to explore efficiently the parameter search space. We have conducted an extensive experimental investigation in the Berkeley Pacman environment which con rms that AC tools can provide up to an additional 20% boost in performance to DQL agents.ca_ES
dc.format.extent46 p.ca_ES
dc.language.isoengca_ES
dc.rightscc-by-nc-ndca_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAutomatic configurationca_ES
dc.subjectReinforcement learningca_ES
dc.subjectDeep Q-learningca_ES
dc.subject.otherAprenentatge automàticca_ES
dc.titleAutomatically Configuring Deep Q-Learning agents for the Berkeley Pacman projectca_ES
dc.typeinfo:eu-repo/semantics/masterThesisca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

cc-by-nc-nd
Except where otherwise noted, this item's license is described as cc-by-nc-nd