Show simple item record

dc.contributor.author
Meier, Robert
dc.contributor.author
Mujika, Asier
dc.contributor.editor
Koyejo, Sanmi
dc.contributor.editor
Mohamed, Shakir
dc.contributor.editor
Agarwal, Alekh
dc.contributor.editor
Belgrave, Danielle
dc.contributor.editor
Cho, Kyunghyun
dc.contributor.editor
Oh, Alice
dc.date.accessioned
2023-04-05T06:28:53Z
dc.date.available
2023-01-17T16:19:23Z
dc.date.available
2023-02-15T11:00:09Z
dc.date.available
2023-04-05T06:28:53Z
dc.date.issued
2022
dc.identifier.isbn
978-1-7138-7108-8
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/593151
dc.description.abstract
Inspired by the great success of unsupervised learning in Computer Vision and Natural Language Processing, the Reinforcement Learning community has recently started to focus more on unsupervised discovery of skills. Most current approaches, like DIAYN or DADS, optimize some form of mutual information objective. We propose a different approach that uses reward functions encoded by neural networks. These are trained iteratively to reward more complex behavior. In high-dimensional robotic environments our approach learns a wide range of interesting skills including front-flips for Half-Cheetah and one-legged running for Humanoid. In the pixel-based Montezuma's Revenge environment our method also works with minimal changes and it learns complex skills that involve interacting with items and visiting diverse locations.
en_US
dc.language.iso
en
en_US
dc.publisher
Curran
en_US
dc.title
Open-Ended Reinforcement Learning with Neural Reward Functions
en_US
dc.type
Conference Paper
ethz.book.title
Advances in Neural Information Processing Systems 35
en_US
ethz.pages.start
2465
en_US
ethz.pages.end
2479
en_US
ethz.event
36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022)
en_US
ethz.event.location
New Orleans, LA, USA
en_US
ethz.event.date
November 28 - December 9, 2022
en_US
ethz.notes
Poster presentation on November 29, 2022.
en_US
ethz.grant
Temporal Information Integration in Neural Networks
en_US
ethz.publication.place
Red Hook, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02643 - Institut für Theoretische Informatik / Inst. Theoretical Computer Science::03672 - Steger, Angelika / Steger, Angelika
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02643 - Institut für Theoretische Informatik / Inst. Theoretical Computer Science::03672 - Steger, Angelika / Steger, Angelika
en_US
ethz.identifier.url
https://proceedings.neurips.cc/paper_files/paper/2022/hash/10a6bdcabbd5a3d36b760daa295f63c1-Abstract-Conference.html
ethz.identifier.url
https://nips.cc/virtual/2022/poster/55000
ethz.grant.agreementno
173721
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Sinergia
ethz.date.deposited
2023-01-17T16:19:23Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2023-04-05T06:28:54Z
ethz.rosetta.lastUpdated
2023-04-05T06:28:54Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Open-Ended%20Reinforcement%20Learning%20with%20Neural%20Reward%20Functions&rft.date=2022&rft.spage=2465&rft.epage=2479&rft.au=Meier,%20Robert&Mujika,%20Asier&rft.isbn=978-1-7138-7108-8&rft.genre=proceeding&rft.btitle=Advances%20in%20Neural%20Information%20Processing%20Systems%2035
 Search print copy at ETH Library

Files in this item

FilesSizeFormatOpen in viewer

There are no files associated with this item.

Publication type

Show simple item record