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Displaying 31 - 38 of 38
Measure-Valued Derivatives for Reinforcement Learning
Reinforcement learning methods for robotics are increasingly successful due to the constant development of better policy ...
IngenieurwissenschaftenInformatik
Multi-Scale Modeling of Additive Manufactured Magnetic Materials
In 2018, Gottschall et al. proposed a multi-stimulit concept for magnetocaloric cooling. [1] The energy consumption ...
IngenieurwissenschaftenMaterialwissenschaft
When to Trust Your Model in Model-based Value Expansion?
Model-based value expansion methods promise to improve the quality of value function targets and, thereby, the ...
IngenieurwissenschaftenInformatik
Balloon Estimators for Improving and Scaling the Nonparametric Off-Policy Policy Gradient
The Nonparametric Off-Policy Policy Gradient (NOPG) is a policy gradient algorithm to solve reinforcement learning tasks ...
IngenieurwissenschaftenInformatik
Constraint Space Projection Reinforcement Learning
The aim of the project is to develop a new family of optimization algorithms to handle convex constraints and evaluate ...
IngenieurwissenschaftenInformatik-
Evaluating and Improving Recurrent Kalman Networks
Precise models of the system dynamics are crucial for model-based control and reinforcement learning (RL) in autonomous ...
IngenieurwissenschaftenInformatik -
Multiscale Simulations Toward Predicting Temperature Dependent Properties of Magnetic Materials
Magnetic materials have been widely used in the modern society due to their intriguing functional properties. For ...
IngenieurwissenschaftenMaterialwissenschaft -
Efficient representations of deep neural networks
Deep neural networks (DNNs) have gained extreme popularity in recent years, advancing state-of-the- art results in ...
IngenieurwissenschaftenInformatik
