Agnostic System Identification for Reinforcement Learning
GPTKB entity
Statements (15)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:academic_journal
|
| gptkbp:allows |
The paper proposes a method for system identification in reinforcement learning that does not require prior knowledge of the system class.
|
| gptkbp:author |
gptkb:Christian_Luck
gptkb:Michael_Lutter gptkb:Jan_Peters |
| gptkbp:citation |
over 50 (as of 2024)
|
| gptkbp:field |
gptkb:Machine_Learning
gptkb:Reinforcement_Learning System Identification |
| https://www.w3.org/2000/01/rdf-schema#label |
Agnostic System Identification for Reinforcement Learning
|
| gptkbp:publicationYear |
2020
|
| gptkbp:publishedIn |
gptkb:Proceedings_of_the_37th_International_Conference_on_Machine_Learning_(ICML_2020)
|
| gptkbp:url |
https://proceedings.mlr.press/v119/lutter20a.html
|
| gptkbp:bfsParent |
gptkb:Lihong_Li
|
| gptkbp:bfsLayer |
6
|