Towards evaluating policy optimisation agents using algorithmic intelligence quotient test
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61384399%3A31140%2F24%3A00059927" target="_blank" >RIV/61384399:31140/24:00059927 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-50396-2_25" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-50396-2_25</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-50396-2_25" target="_blank" >10.1007/978-3-031-50396-2_25</a>
Alternative languages
Result language
angličtina
Original language name
Towards evaluating policy optimisation agents using algorithmic intelligence quotient test
Original language description
Main topics of the document: reinforcement learning; vanilla policy gradient; proximal policy optimisation; evaluating intelligence of artificial systems; universal intelligence definition; algorithmic intelligence quotient test
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Intelligence. ECAI 2023 International Workshops
ISBN
978-3-031-50395-5
ISSN
1865-0929
e-ISSN
1865-0937
Number of pages
17
Pages from-to
435-451
Publisher name
Springer Cham
Place of publication
Germany
Event location
Krakow
Event date
Sep 30, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001259329400025