Towards a deep reinforcement learning integration into model-based systems engineering
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource, 20 Seiten
- Sprache
-
Englisch
- Anmerkungen
-
In: Engineering for a Changing World
In: Ilmenau Scientific Colloquium. Technische Universität Ilmenau ; 60 (Ilmenau) : 2023.09.04-08
In: ilmedia, Ilmenau
- Ereignis
-
Veröffentlichung
- (wo)
-
Ilmenau
- (wer)
-
TU Ilmenau
- (wann)
-
2023
- URN
-
urn:nbn:de:gbv:ilm1-2023isc-100:4
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:51 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
- Trentsios, Pascalis
- Wolf, Mario
- Gerhard, Detlef
- TU Ilmenau
Entstanden
- 2023
Ähnliche Objekte (12)
Sample-efficient deep reinforcement learning via automatic curriculum learning and model-based reinforcement learning
Model-based deep reinforcement learning for accelerated learning from flow simulations
Towards designing a generic and comprehensive deep reinforcement learning framework
Stable deep reinforcement learning
Policy Regularization for Model-Based Offline Reinforcement Learning
Towards a collaborative working environment to support model-based systems engineering
Towards biologically plausible model-based reinforcement learning in recurrent spiking networks by dreaming new experiences
Deep Model-Based Reinforcement Learning for Predictive Control of Robotic Systems with Dense and Sparse Rewards
Interpolation assisted deep reinforcement learning
Explainability in Deep Reinforcement Learning
Data-efficient model-based reinforcement learning with trajectory discrimination
Model-Based Engineering mit Industriesteuerungen
Sample-efficient deep reinforcement learning via automatic curriculum learning and model-based reinforcement learning
Model-based deep reinforcement learning for accelerated learning from flow simulations
Towards designing a generic and comprehensive deep reinforcement learning framework
Stable deep reinforcement learning
Policy Regularization for Model-Based Offline Reinforcement Learning
Towards a collaborative working environment to support model-based systems engineering
Towards biologically plausible model-based reinforcement learning in recurrent spiking networks by dreaming new experiences
Deep Model-Based Reinforcement Learning for Predictive Control of Robotic Systems with Dense and Sparse Rewards
Interpolation assisted deep reinforcement learning
Explainability in Deep Reinforcement Learning
Data-efficient model-based reinforcement learning with trajectory discrimination
Model-Based Engineering mit Industriesteuerungen
Sample-efficient deep reinforcement learning via automatic curriculum learning and model-based reinforcement learning
Model-based deep reinforcement learning for accelerated learning from flow simulations
Towards designing a generic and comprehensive deep reinforcement learning framework
Stable deep reinforcement learning
Policy Regularization for Model-Based Offline Reinforcement Learning
Towards a collaborative working environment to support model-based systems engineering
Towards biologically plausible model-based reinforcement learning in recurrent spiking networks by dreaming new experiences
Deep Model-Based Reinforcement Learning for Predictive Control of Robotic Systems with Dense and Sparse Rewards
Interpolation assisted deep reinforcement learning
Explainability in Deep Reinforcement Learning
Data-efficient model-based reinforcement learning with trajectory discrimination