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Publikationen

Zeige Ergebnisse 51 bis 58 von 58
  1. Hirotaka Hachiya; Takayuki Akiyama; Masashi Sugiyama; Jan Peters

    Efficient data reuse in value function approximation

    In: 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL 2009) Proceedings. IEEE Symposium on Adaptive Dynamic …

  2. Matthew Hoffman; Nando de Freitas; Arnaud Doucet; Jan Peters

    An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward

    In: David A. Van Dyk; Max Welling (Hrsg.). Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics. …

  3. Jan Peters; Jun Morimoto; Russ Tedrake; Nicholas Roy

    Robot learning [TC Spotlight]

    In: IEEE Robotics & Automation Magazine, Vol. 16, No. 3, Pages 19-20, IEEE, 2009.

  4. Hirotaka Hachiya; Takayuki Akiyama; Masashi Sugiyama; Jan Peters

    Adaptive importance sampling for value function approximation in off-policy reinforcement learning

    In: Neural Networks, Vol. 22, No. 10, Pages 1399-1410, Elsevier, 2009.

  5. Marc Peter Deisenroth; Carl Edward Rasmussen; Jan Peters

    Gaussian process dynamic programming

    In: Neurocomputing, Vol. 72, No. 7-9, Pages 1508-1524, Elsevier, 2009.

  6. Jan Peters; Andrew Y. Ng

    Guest editorial: Special issue on robot learning, Part B

    In: Autonomous Robots, Vol. 27, No. 2, Pages 91-92, Springer, 2009.

  7. Jan Peters; Andrew Y. Ng

    Guest editorial: Special issue on robot learning, Part A

    In: Autonomous Robots, Vol. 27, No. 1, Pages 1-2, Springer, 2009.

  8. Duy Nguyen-Tuong; Matthias W. Seeger; Jan Peters

    Model Learning with Local Gaussian Process Regression

    In: Advanced Robotics, Vol. 23, No. 15, Pages 2015-2034, Taylor & Francis Online, 2009.