- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
Hamburg, Universität Hamburg, Dissertation, 2022
- Klassifikation
-
Elektrotechnik, Elektronik
- Schlagwort
-
Reinforcement learning
- Ereignis
-
Veröffentlichung
- (wo)
-
Hamburg
- (wer)
-
Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
- (wann)
-
2021
- Urheber
-
Cong, Lin
- Beteiligte Personen und Organisationen
- URN
-
urn:nbn:de:gbv:18-ediss-98526
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:36 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Cong, Lin
- Zhang, Jianwei
- Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
Entstanden
- 2021
Ähnliche Objekte (12)
On decomposability in robot reinforcement learning
Real-time robot personality adaptation based on reinforcement learning and social signals
Robot manipulation of deformable objects
Decidability in robot manipulation planning
Robot Manipulation of Deformable Objects
Precise atom manipulation through deep reinforcement learning
Autonomous robot car with Deep Reinforcement Learning
Autonomous robot car with Deep Reinforcement Learning
Cognitive Reasoning for Compliant Robot Manipulation
Guiding real-world reinforcement learning for in-contact manipulation tasks with Shared Control Templates
A Reinforcement Learning Approach for Continuum Robot Control
Sequencing of multi-robot behaviors using reinforcement learning
On decomposability in robot reinforcement learning
Real-time robot personality adaptation based on reinforcement learning and social signals
Robot manipulation of deformable objects
Decidability in robot manipulation planning
Robot Manipulation of Deformable Objects
Precise atom manipulation through deep reinforcement learning
Autonomous robot car with Deep Reinforcement Learning
Autonomous robot car with Deep Reinforcement Learning
Cognitive Reasoning for Compliant Robot Manipulation
Guiding real-world reinforcement learning for in-contact manipulation tasks with Shared Control Templates
A Reinforcement Learning Approach for Continuum Robot Control
Sequencing of multi-robot behaviors using reinforcement learning
On decomposability in robot reinforcement learning
Real-time robot personality adaptation based on reinforcement learning and social signals
Robot manipulation of deformable objects
Decidability in robot manipulation planning
Robot Manipulation of Deformable Objects
Precise atom manipulation through deep reinforcement learning
Autonomous robot car with Deep Reinforcement Learning
Autonomous robot car with Deep Reinforcement Learning
Cognitive Reasoning for Compliant Robot Manipulation
Guiding real-world reinforcement learning for in-contact manipulation tasks with Shared Control Templates
A Reinforcement Learning Approach for Continuum Robot Control