8 citations · 19 across the 5 of their papers we have counts for
5 papers
Designing a Robust Low-Level Agnostic Controller for a Quadrotor with Actor-Critic Reinforcement Learning
Guilherme Siqueira Eduardo, Wouter Caarls
Purpose: Real-life applications using quadrotors introduce a number of disturbances and time-varying properties that pose a challenge to flight controllers. We observed that, when…
Online Weighted Q-Ensembles for Reduced Hyperparameter Tuning in Reinforcement Learning
Renata Garcia, Wouter Caarls
Reinforcement learning is a promising paradigm for learning robot control, allowing complex control policies to be learned without requiring a dynamics model. However, even state o…
Deep Reinforcement Learning with Embedded LQR Controllers
Wouter Caarls
Reinforcement learning is a model-free optimal control method that optimizes a control policy through direct interaction with the environment. For reaching tasks that end in regula…
Deep Reinforcement Learning for Haptic Shared Control in Unknown Tasks
Franklin Cardeñoso Fernandez, Wouter Caarls
Recent years have shown a growing interest in using haptic shared control (HSC) in teleoperated systems. In HSC, the application of virtual guiding forces decreases the user's cont…
Q-caching: an integrated reinforcement-learning approach for caching and routing in information-centric networks
Wouter Caarls, Eduardo Hargreaves, Daniel S. Menasché
Content delivery, such as video streaming, is one of the most prevalent Internet applications. Although very popular, the continuous growth of such applications poses novel perform…