Blar i NTNU Open på forfatter "Bøhn, Eivind Eigil"

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    • Accelerating Reinforcement Learning with Suboptimal Guidance 

      Bøhn, Eivind Eigil; Moe, Signe; Johansen, Tor Arne (Peer reviewed; Journal article, 2020)
      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 random fashion for learning signals. For ...

    • Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments 

      Bøhn, Eivind Eigil; Coates, Erlend M.; Reinhardt, Dirk Peter; Johansen, Tor Arne (Peer reviewed; Journal article, 2023)
      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 longitudinal and lateral motions. Current ...

    • Deep Reinforcement Learning Attitude Control of Fixed Wing UAVs Using Proximal Policy Optimization 

      Bøhn, Eivind Eigil; Coates, Erlend Magnus Lervik; Moe, Signe; Johansen, Tor Arne (Chapter, 2019)
      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 conditions UAVs can operate in and the types ...

    • Optimization of the model predictive control meta-parameters through reinforcement learning 

      Bøhn, Eivind Eigil; Gros, Sebastien Nicolas; Moe, Signe; Johansen, Tor Arne (Peer reviewed; Journal article, 2023)
      Model predictive control (MPC) is increasingly being considered for control of fast systems and embedded applications. However, MPC has some significant challenges for such systems, such as its high computational complexity. ...

    • Pseudo-Hamiltonian neural networks with state-dependent external forces 

      Stasik, Alexander Johannes; Sterud, Camilla; Bøhn, Eivind Eigil; Riemer-Sørensen, Signe (Peer reviewed; Journal article, 2023)
      Hybrid machine learning based on Hamiltonian formulations has recently been successfully demonstrated for simple mechanical systems, both energy conserving and not energy conserving. We introduce a pseudo-Hamiltonian ...

    • Semantic Segmentation of Radar Data with Deep Learning 

      Bøhn, Eivind Eigil (Master thesis, 2018)
      Autonomous ships have the potential to redefine the maritime industry, providing substantial improvements in safety, economics and fuel efficiency, while creating new previously unimagined services. Radar is a key part of ...