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Displaying 31 - 42 of 42
Deep Reinforcement Learning: Benchmarking, Algorithms and Applications
Deep Learning is the major component of the success of most new Artificial Intelligence applications. A new promising ...
Ingenieurwissenschaften
Designing an Efficient Neural Architecture Search
Within the last decade, deep neural networks have attracted much attention from academia and industry. Such a wide ...
IngenieurwissenschaftenInformatik
Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System
Normative computational models of human sensorimotor behavior based on optimal feedback control with signal-dependent ...
LebenswissenschaftenNeurowissenschaft
Learning Physically Interactive Behaviours for Human-Robot Interaction
Modeling interaction dynamics to generate robot trajectories that enable a robot to adapt and react to a human’s actions ...
IngenieurwissenschaftenInformatik
Measure-Valued Derivatives for Reinforcement Learning
Reinforcement learning methods for robotics are increasingly successful due to the constant development of better policy ...
IngenieurwissenschaftenInformatik
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 
Interpretable Reinforcement Learning
Deep Reinforcement Learning can solve difficult high dimensional tasks, by exploiting the expressive representation ...
Engineering SciencesComputer Science-
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
