@inproceedings{SpitznagelWeilerDorer2021,
  author    = {Martin Spitznagel and David Weiler and Klaus Dorer},
  title     = {Deep Reinforcement Multi-Directional Kick-Learning of a Simulated Robot with Toes},
  series = {2021 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)},
  editor    = {Vitor Santos and Nuno Lau and Pedro Neto and Ana Cristina Lopes},
  publisher = {IEEE},
  isbn      = {978-1-6654-3198-9 (elektronisch)},
  doi       = {10.1109/ICARSC52212.2021.9429811},
  pages     = {104 -- 110},
  year      = {2021},
  abstract  = {This paper describes a thorough analysis of using PPO to learn kick behaviors with simulated NAO robots in the simspark environment. The analysis includes an investigation of the influence of PPO hyperparameters, network size, training setups and performance in real games. We believe to improve the state of the art mainly in four points: first, the kicks are learned with a toed version of the NAO robot, second, we improve the reliability with respect to kickable area and avoidance of falls, third, the kick can be parameterized with desired distance and direction as input to the deep network and fourth, the approach allows to integrate the learned behavior seamlessly into soccer games. The result is a significant improvement of the general level of play.},
  language  = {en}
}