219 citations · 220 across the 3 of their papers we have counts for
6 papers
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…
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…
Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization
Eivind Bøhn, Erlend M. Coates, Signe Moe +1
Contemporary autopilot systems for unmanned aerial vehicles (UAVs) are far more limited in their flight envelope as compared to experienced human pilots, thereby restricting the co…
Set-based Control for Autonomous Spray Painting
Signe Moe, Jan Tommy Gravdahl, Kristin Y. Pettersen
In this paper, a method is presented for lowering the energy consumption and/or increasing the speed of a standard manipulator spray painting a surface. The approach is based on th…