def oneUpdate(directory,plot_results=False):
inputs = np.linspace(-1.5,1.5,21);
a = 2.0
c = -1.0
n_params = 2
regularization = 0.01
task = DemoTaskApproximateQuadraticFunction(a,c,inputs,regularization)
mean_init = np.full(n_params,0.5)
covar_init = 0.25*np.eye(n_params)
initial_distribution = DistributionGaussian(mean_init, covar_init)
eliteness = 10
weighting_method = 'PI-BB'
covar_decay_factor = 0.8
updater = UpdaterCovarDecay(eliteness,weighting_method,covar_decay_factor)
n_samples_per_update = 10
i_update = runOptimizationTaskOneUpdate(directory, task, initial_distribution, updater, n_samples_per_update)
i_update -= 1
if plot_results and i_update>1:
# Plot the optimization results (from the files saved to disk)
fig = plt.figure(1,figsize=(15, 5))
plotOptimizationRollouts(directory,fig,task.plotRollout)
plt.show()
learning_rate = 0.5
#updater = UpdaterCovarAdaptation(eliteness, weighting_method)
updater_adaptation = UpdaterCovarAdaptation(eliteness, weighting_method,
max_level, min_level,
diag_only, learning_rate)
updater = updater_decay
n_samples_per_update = 5
############################################################
# Load the initial distribution
input_mean_file = directory + '/policy_parameters.txt'
input_covar_file = directory + '/distribution_initial_covar.txt'
print('Python | Loading distribution from "' + input_mean_file +
'" and "' + input_covar_file + '"')
mean_init = np.loadtxt(input_mean_file)
covar_init = np.loadtxt(input_covar_file)
initial_distribution = DistributionGaussian(mean_init, covar_init)
# Load the task
task = pickle.load(open(directory + '/task.p', "rb"))
# Execute one update
i_update = runOptimizationTaskOneUpdate(directory, task,
initial_distribution, updater,
n_samples_per_update)
#print("./robotPerformRollouts.bash $DIRECTORY/dmp.xml ${UPDATE_DIR}")