(self, algorithm, agent, m)
Update the linear Guassian controller plots with iteration data,
        for the mean and covariances of the end effector points.
Update the linear Guassian controller plots with iteration data,
        for the mean and covariances of the end effector points.
Update the linear Guassian controller plots with iteration data for the mean and covariances of the end effector points .
def _update_linear_gaussian_controller_plots(self, algorithm, agent, m):
        """
        Update the linear Guassian controller plots with iteration data,
        for the mean and covariances of the end effector points.
        """
        # Calculate mean and covariance for end effector points
        eept_idx = agent.get_idx_x(END_EFFECTOR_POINTS)
        start, end = eept_idx[0], eept_idx[-1]
        mu, sigma = algorithm.traj_opt.forward(algorithm.prev[m].traj_distr, algorithm.prev[m].traj_info)
        mu_eept, sigma_eept = mu[:, start:end+1], sigma[:, start:end+1, start:end+1]

        # Linear Gaussian Controller Distributions (Red)
        for i in range(mu_eept.shape[1]/3):
            mu, sigma = mu_eept[:, 3*i+0:3*i+3], sigma_eept[:, 3*i+0:3*i+3, 3*i+0:3*i+3]
            self._traj_visualizer.plot_3d_gaussian(i=m, mu=mu, sigma=sigma,
                    edges=100, linestyle='-', linewidth=1.0, color='red',
                    alpha=0.15, label='LG Controller Distributions')

        # Linear Gaussian Controller Means (Dark Red)
        for i in range(mu_eept.shape[1]/3):
            mu = mu_eept[:, 3*i+0:3*i+3]
            self._traj_visualizer.plot_3d_points(i=m, points=mu, linestyle='None',
                    marker='x', markersize=5.0, markeredgewidth=1.0,
                    color=(0.5, 0, 0), alpha=1.0, label='LG Controller Means')
def _update_linear_gaussian_controller_plots ( self , algorithm , agent , m ) : # Calculate mean and covariance for end effector points eept_idx = agent . get_idx_x ( END_EFFECTOR_POINTS ) start , end = eept_idx [ 0 ] , eept_idx [ - 1 ] mu , sigma = algorithm . traj_opt . forward ( algorithm . prev [ m ] . traj_distr , algorithm . prev [ m ] . traj_info ) mu_eept , sigma_eept = mu [ : , start : end + 1 ] , sigma [ : , start : end + 1 , start : end + 1 ] # Linear Gaussian Controller Distributions (Red) for i in range ( mu_eept . shape [ 1 ] / 3 ) : mu , sigma = mu_eept [ : , 3 * i + 0 : 3 * i + 3 ] , sigma_eept [ : , 3 * i + 0 : 3 * i + 3 , 3 * i + 0 : 3 * i + 3 ] self . _traj_visualizer . plot_3d_gaussian ( i = m , mu = mu , sigma = sigma , edges = 100 , linestyle = '-' , linewidth = 1.0 , color = 'red' , alpha = 0.15 , label = 'LG Controller Distributions' ) # Linear Gaussian Controller Means (Dark Red) for i in range ( mu_eept . shape [ 1 ] / 3 ) : mu = mu_eept [ : , 3 * i + 0 : 3 * i + 3 ] self . _traj_visualizer . plot_3d_points ( i = m , points = mu , linestyle = 'None' , marker = 'x' , markersize = 5.0 , markeredgewidth = 1.0 , color = ( 0.5 , 0 , 0 ) , alpha = 1.0 , label = 'LG Controller Means' )