219 citations · 312 across the 9 of their papers we have counts for
12 papers
A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction
Jon Alvarez Justo, Daniela Lupu, Milica Orlandic +2
Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission. Compressive Sensing has been used in th…
Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning
Eivind Bøhn, Sebastien Gros, Signe Moe +1
Model predictive control (MPC) is increasingly being considered for control of fast systems and embedded applications. However, the MPC has some significant challenges for such sys…
Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments
Eivind Bøhn, Erlend M. Coates, Dirk Reinhardt +1
Attitude control of fixed-wing unmanned aerial vehicles (UAVs) is a difficult control problem in part due to uncertain nonlinear dynamics, actuator constraints, and coupled longitu…
Reinforcement Learning of the Prediction Horizon in Model Predictive Control
Eivind Bøhn, Sebastien Gros, Signe Moe +1
Model predictive control (MPC) is a powerful trajectory optimization control technique capable of controlling complex nonlinear systems while respecting system constraints and ensu…
Optimization of the Model Predictive Control Update Interval Using Reinforcement Learning
Eivind Bøhn, Sebastien Gros, Signe Moe +1
In control applications there is often a compromise that needs to be made with regards to the complexity and performance of the controller and the computational resources that are…
Accelerating Reinforcement Learning with Suboptimal Guidance
Eivind Bøhn, Signe Moe, Tor Arne Johansen
Reinforcement Learning in domains with sparse rewards is a difficult problem, and a large part of the training process is often spent searching the state space in a more or less ra…