• 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 ...
    • 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 ...
    • 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 ...