Intrinsically Motivated Reinforcement Learning: A Promising Framework for Procedural Content Generation

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Original languageEnglish
Title of host publicationIEEE Computational Inteligence in Games
PublisherIEEE
Publication date2016
Publication statusPublished - 2016
EventIEEE Computational Inteligence in Games -
Duration: 20 Sep 201623 Sep 2016

Conference

ConferenceIEEE Computational Inteligence in Games
Period20/09/201623/09/2016

Cite this

@inproceedings{b80c6fb6c71f4af2b4e7f917e88a031c,
title = "Intrinsically Motivated Reinforcement Learning: A Promising Framework for Procedural Content Generation",
author = "Noor Shaker",
year = "2016",
language = "English",
booktitle = "IEEE Computational Inteligence in Games",
publisher = "IEEE",
address = "United States",

}

Shaker, N 2016, Intrinsically Motivated Reinforcement Learning: A Promising Framework for Procedural Content Generation. in IEEE Computational Inteligence in Games. IEEE, IEEE Computational Inteligence in Games, 20/09/2016.

Intrinsically Motivated Reinforcement Learning : A Promising Framework for Procedural Content Generation. / Shaker, Noor.

IEEE Computational Inteligence in Games. IEEE, 2016.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

TY - GEN

T1 - Intrinsically Motivated Reinforcement Learning

T2 - A Promising Framework for Procedural Content Generation

AU - Shaker, Noor

PY - 2016

Y1 - 2016

M3 - Article in proceeding

BT - IEEE Computational Inteligence in Games

PB - IEEE

ER -