def _setup_learning(self, representation):
        # multiplies each dimension, used to make displayed grid appear finer:
        # plotted gridcell values computed through interpolation nearby according
        # to representation.Qs(s)
        granularity = 5.

        # TODO: currently we only allow for equal discretization in all
        # dimensions.  In future, must modify below code to handle non-uniform
        # discretization.
        thetaDiscr = representation.discretization
        thetaDotDiscr = representation.discretization

        # Create the center of the grid cells both in theta and
        # thetadot_dimension
        theta_binWidth = (
            self.ANGLE_LIMITS[1] - self.ANGLE_LIMITS[0]) / (thetaDiscr * granularity)
        thetas = np.linspace(
            self.ANGLE_LIMITS[0] + theta_binWidth / 2,
            self.ANGLE_LIMITS[1] - theta_binWidth / 2,
            thetaDiscr * granularity)
        theta_dot_binWidth = (
            self.ANGULAR_RATE_LIMITS[1] - self.ANGULAR_RATE_LIMITS[0]) / (thetaDotDiscr * granularity)
        theta_dots = np.linspace(
            self.ANGULAR_RATE_LIMITS[0] + theta_dot_binWidth / 2,
            self.ANGULAR_RATE_LIMITS[1] - theta_dot_binWidth / 2,
            thetaDotDiscr * granularity)

        self.xTicks = np.linspace(0, granularity * thetaDiscr - 1, 5)
        self.yTicks = np.linspace(0, granularity * thetaDotDiscr - 1, 5)

        return (thetas, theta_dots)
Initializes the arrays of ``theta`` and ``thetaDot`` values we will
dataset/ETHPy150Open rlpy/rlpy/rlpy/Domains/CartPoleBase.py/CartPoleBase._setup_learning