Deep reinforcement learning for physical agents

  • Arndt, Karol (Project Member)
  • Ghadirzadeh, Ali (Project Member)
  • Hazara, Murtaza (Project Member)
  • Kyrki, Ville (Principal investigator)
  • Struckmeier, Oliver (Project Member)

Project Details

Short titleDeepen
StatusFinished
Effective start/end date01/01/201831/12/2019
  • Few-shot model-based adaptation in noisy conditions

    Arndt, K., Ghadirzadeh, A., Hazara, M. & Kyrki, V., Apr 2021, In: IEEE Robotics and Automation Letters. 6, 2, p. 4193-4200 8 p., 9384205.

    Research output: Contribution to journalArticleScientificpeer-review

    Open Access
    File
    18 Downloads (Pure)
  • Meta Reinforcement Learning for Sim-to-real Domain Adaptation

    Arndt, K., Hazara, M., Ghadirzadeh, A. & Kyrki, V., 2020, Proceedings of the IEEE Conference on Robotics and Automation, ICRA 2020. IEEE, p. 2725-2731 7 p. 9196540. (IEEE International Conference on Robotics and Automation).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Open Access
    4 Citations (Scopus)
  • Affordance Learning for End-to-End Visuomotor Robot Control

    Hamalainen, A., Arndt, K., Ghadirzadeh, A. & Kyrki, V., 1 Nov 2019, Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019. IEEE, p. 1781-1788 8 p. 8968596. (Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Open Access
    4 Citations (Scopus)