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  • Conference paper
    Ahmadzadeh SR, Leonetti M, Kormushev P, 2013,

    Online Direct Policy Search for Thruster Failure Recovery in Autonomous Underwater Vehicles

  • Conference paper
    Jamali N, Kormushev P, Caldwell DG, 2013,

    Contact State Estimation using Machine Learning

  • Conference paper
    Kormushev P, Caldwell DG, 2013,

    Comparative Evaluation of Reinforcement Learning with Scalar Rewards and Linear Regression with Multidimensional Feedback

  • Conference paper
    Kormushev P, Caldwell DG, 2013,

    Towards Improved AUV Control Through Learning of Periodic Signals

  • Book
    Deisenroth MP, Neumann G, Peters J, 2013,

    A Survey on Policy Search for Robotics

    , Publisher: now Publishers

    Policy search is a subfield in reinforcement learning which focuses onfinding good parameters for a given policy parametrization. It is wellsuited for robotics as it can cope with high-dimensional state and actionspaces, one of the main challenges in robot learning. We review recentsuccesses of both model-free and model-based policy search in robotlearning.Model-free policy search is a general approach to learn policiesbased on sampled trajectories. We classify model-free methods based ontheir policy evaluation strategy, policy update strategy, and explorationstrategy and present a unified view on existing algorithms. Learning apolicy is often easier than learning an accurate forward model, and,hence, model-free methods are more frequently used in practice. How-ever, for each sampled trajectory, it is necessary to interact with the robot, which can be time consuming and challenging in practice. Model-based policy search addresses this problem by first learning a simulatorof the robot’s dynamics from data. Subsequently, the simulator gen-erates trajectories that are used for policy learning. For both model-free and model-based policy search methods, we review their respectiveproperties and their applicability to robotic systems.

  • Conference paper
    Kormushev P, Caldwell DG, 2013,

    Reinforcement Learning with Heterogeneous Policy Representations

  • Conference paper
    Kryczka P, Hashimoto K, Takanishi A, Kormushev P, Tsagarakis N, Caldwell DGet al., 2013,

    Walking Despite the Passive Compliance: Techniques for Using Conventional Pattern Generators to Control Instrinsically Compliant Humanoid Robots

  • Conference paper
    Carrera A, Carreras M, Kormushev P, Palomeras N, Nagappa Set al., 2013,

    Towards valve turning with an AUV using Learning by Demonstration

  • Journal article
    Filippi S, Barnes CP, Cornebise J, Stumpf MPHet al., 2013,

    On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo

    , STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, Vol: 12, ISSN: 2194-6302
  • Journal article
    Silk D, Filippi S, Stumpf MPH, 2013,

    Optimizing threshold-schedules for sequential approximate Bayesian computation: applications to molecular systems

    , STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, Vol: 12, Pages: 603-618, ISSN: 2194-6302

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