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    Direct inverse reinforcement learning with density ratio estimation
    61.
    发明授权
    Direct inverse reinforcement learning with density ratio estimation 有权

    公开(公告)号：US10896383B2

    公开(公告)日：2021-01-19

    申请号：US15425924

    申请日：2017-02-06

    Applicant: Okinawa Institute of Science and Technology School Corporation

    Inventor： Eiji Uchibe ,  Kenji Doya

    IPC: G06N20/00 ,  G06N7/00 ,  G06K9/62

    Abstract: A method of inverse reinforcement learning for estimating reward and value functions of behaviors of a subject includes: acquiring data representing changes in state variables that define the behaviors of the subject; applying a modified Bellman equation given by Eq. (1) to the acquired data: r ⁡ ( x ) + γ ⁢ ⁢ V ⁡ ( y ) - V ⁡ ( x ) = ⁢ ln ⁢ ⁢ π ⁡ ( y | x ) b ⁡ ( y | x ) , ⁢ ( 1 ) = ⁢ ln ⁢ ⁢ π ⁡ ( x , y ) b ⁡ ( x , y ) - ln ⁢ ⁢ π ⁡ ( x ) b ⁡ ( x ) ,                                                ⁢ ( 2 ) where r(x) and V(x) denote a reward function and a value function, respectively, at state x, and γ represents a discount factor, and b(y|x) and π(y|x) denote state transition probabilities before and after learning, respectively; estimating a logarithm of the density ratio π(x)/b(x) in Eq. (2); estimating r(x) and V(x) in Eq. (2) from the result of estimating a log of the density ratio π(x,y)/b(x,y); and outputting the estimated r(x) and V(x).

    2D discrete fourier transform with simultaneous edge artifact removal for real-time applications
    64.
    发明授权
    2D discrete fourier transform with simultaneous edge artifact removal for real-time applications 有权

    公开(公告)号：US10121233B2

    公开(公告)日：2018-11-06

    申请号：US15746407

    申请日：2016-07-20

    Applicant: OKINAWA INSTITUTE OF SCIENCE AND TECHNOLOGY SCHOOL CORPORATION

    Inventor： Faisal Mahmood ,  Märt Toots ,  Lars-Göran Wallentin Öfverstedt ,  Bo Ulf Skoglund

    IPC: G06K9/38 ,  G06T5/10 ,  G06T5/00

    Abstract: A method for performing 2-dimensional discrete Fourier transform of a subject image data to be performed in one or more digital processors includes performing 1-dimensional fast Fourier transform on each row of the subject image data and 1-dimensional fast Fourier transform on each column of the subject image, and performing a simplified fast Fourier transform processing on the extracted boundary image without performing column-by-column 1-dimensional fast Fourier transform by: performing 1-dimensional fast Fourier transform only on a first column vector in the extracted boundary image data, using scaled column vectors to derive fast Fourier transform of remaining columns of the extracted boundary image data, and performing 1-dimensional fast Fourier transform on each row of the extracted boundary image data. Then, fast Fourier transform of a periodic component of the subject image data with edge-artifacts removed and fast Fourier transform of a smooth component of the subject image data are derived from results of steps (b) and (c).

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