def reset(self):
FA.reset(self)
# initialize the LWPR function
self.lwpr = LWPR(self.indim, self.outdim)
self.lwpr.init_D = 10.*np.eye(self.indim)
self.lwpr.init_alpha = 0.1*np.ones([self.indim, self.indim])
self.lwpr.meta = True
class LWPRFA(FA):
parametric = False
def __init__(self, indim, outdim):
FA.__init__(self, indim, outdim)
self.filename = None
def reset(self):
FA.reset(self)
# initialize the LWPR function
self.lwpr = LWPR(self.indim, self.outdim)
self.lwpr.init_D = 10.*np.eye(self.indim)
self.lwpr.init_alpha = 0.1*np.ones([self.indim, self.indim])
self.lwpr.meta = True
def predict(self, inp):
""" predict the output for the given input. """
# the next 3 lines fix a bug when lwpr models are pickled and unpickled again
# without it, a TypeError is thrown "Expected a double precision numpy array."
# even though the numpy array is double precision.
inp = self._asFlatArray(inp)
inp_tmp = np.zeros(inp.shape)
inp_tmp[:] = inp
return self.lwpr.predict(inp_tmp)
def train(self):
for i, t in self.dataset:
i = self._asFlatArray(i)
t = self._asFlatArray(t)
self.lwpr.update(i, t)
def _cleanup(self):
if self.filename and os.path.exists(self.filename):
os.remove(self.filename)
def __getstate__(self):
""" required for pickle. removes the lwpr model from the dictionary
and saves it to file explicitly.
"""
# create unique hash key for filename and write lwpr to file
hashkey = hashlib.sha1(str(self.lwpr) + time.ctime() + str(np.random.random())).hexdigest()[:8]
if not os.path.exists('.lwprmodels'):
os.makedirs('.lwprmodels')
# remove any old files if existing
if self.filename:
os.remove(self.filename)
self.filename = '.lwprmodels/lwpr_%s.binary'%hashkey
self.lwpr.write_binary(self.filename)
# remove lwpr from dictionary and return state
state = self.__dict__.copy()
del state['lwpr']
return state
def __setstate__(self, state):
""" required for pickle. loads the stored lwpr model explicitly.
"""
self.__dict__.update(state)
self.lwpr = LWPR(self.filename)
def __setstate__(self, state):
""" required for pickle. loads the stored lwpr model explicitly.
"""
self.__dict__.update(state)
self.lwpr = LWPR(self.filename)
def test_lwpr_ref(datafile, resultfolder, model_epoch, hist_window, saveFigure=False, modelfile='lwpr_model'):
if (robot_type == 'Vulcan'):
plot_cmd_divider = 100.0
elif (robot_type == 'Fetch') or (robot_type == 'Fetch2') or (robot_type == 'MagicBot'):
plot_cmd_divider = 1.0
# load data file
dataset = loadmat(datafile)
test_data_x = dataset['reference_input']
num_test, test_dim = test_data_x.shape[0], 125
speed_hw, cmd_hw = hist_window[0], hist_window[1]
input_dim = 2*(speed_hw+cmd_hw)
# load model files
modelfile_name = './' + resultfolder + '/' + modelfile + '_best_left_epoch' + str(model_epoch) + '.bin'
best_left_model = LWPR(modelfile_name)
print 'Read Left model (%d)' % (best_left_model.num_rfs[0])
modelfile_name = './' + resultfolder + '/' + modelfile + '_best_right_epoch' + str(model_epoch) + '.bin'
best_right_model = LWPR(modelfile_name)
print 'Read Right model (%d)' % (best_right_model.num_rfs[0])
# start making 5 seconds simulation
pred_on_test = np.zeros((num_test, 2*test_dim))
tmp_x = np.zeros((input_dim, 1))
pred_left_speed = np.zeros((num_test, 125))
pred_right_speed = np.zeros((num_test, 125))
forward_cmd = np.zeros((num_test, 125))
left_cmd = np.zeros((num_test, 125))
print 'start prediction on reference input'
start_test_time = timeit.default_timer()
for data_cnt in range(num_test):
for pred_cnt in range(test_dim):
if pred_cnt < speed_hw:
num_value_from_data = 2*(speed_hw-pred_cnt)
tmp_x[0:num_value_from_data, 0] = test_data_x[data_cnt, 2*pred_cnt:2*speed_hw]
tmp_x[num_value_from_data:2*speed_hw, 0] = pred_on_test[data_cnt, 0:2*pred_cnt]
else:
tmp_x[0:2*speed_hw, 0] = pred_on_test[data_cnt, 2*(pred_cnt-speed_hw):2*pred_cnt]
tmp_x[2*speed_hw:input_dim, 0] = test_data_x[data_cnt, 2*(speed_hw+pred_cnt):2*(speed_hw+pred_cnt+cmd_hw)]
pred_on_test[data_cnt, 2*pred_cnt], _ = best_left_model.predict_conf(tmp_x)
pred_on_test[data_cnt, 2*pred_cnt+1], _ = best_right_model.predict_conf(tmp_x)
tmp_cmd = test_data_x[data_cnt, (input_dim-2):(input_dim-2+250)].reshape((125, 2))
tmp_pred = pred_on_test[data_cnt, :].reshape((125, 2))
pred_left_speed[data_cnt,:] = tmp_pred[:,0]
pred_right_speed[data_cnt,:] = tmp_pred[:,1]
forward_cmd[data_cnt,:] = tmp_cmd[:,0]
left_cmd[data_cnt,:] = tmp_cmd[:,1]
if data_cnt%1000 == 0:
print '\t\t', data_cnt
end_test_time = timeit.default_timer()
print 'finish prediction on reference input'
save_file_name = './' + resultfolder + '/ref_input_result_LWPR.mat'
result={}
result['test_time'] = end_test_time - start_test_time
result['model_output_on_test_data'] = pred_on_test
result['joystick_command'] = test_data_x[:, (input_dim-2):(input_dim-2+250)]
savemat(save_file_name, result)
# plot (lots of) graphs
if saveFigure:
plot_path = os.getcwd()
plot_path = plot_path + '/' + resultfolder
if not('RefResponsePlot_LWPR' in os.listdir(plot_path)):
dir_path = plot_path + '/RefResponsePlot_LWPR'
os.mkdir(dir_path)
pdf_name = plot_path + '/LWPR_ref_response_plot.pdf'
pp = PdfPages(pdf_name)
for cnt in range(num_test):
plot_x = np.linspace(0, 5, num=125, endpoint=True)
plot_y1 = pred_left_speed[cnt,:]
plot_y2 = pred_right_speed[cnt,:]
plot_cmd1 = forward_cmd[cnt,:]/plot_cmd_divider
plot_cmd2 = left_cmd[cnt,:]/plot_cmd_divider
title1 = 'Left Wheel response to ref input ' + str(cnt)
title2 = 'Right Wheel response to ref input ' + str(cnt)
filename = plot_path + '/RefResponsePlot_LWPR/' + str(cnt) + '.png'
#ymax1 = max(np.amax(plot_y1)+0.1, np.amax(plot_cmd1)+0.1, np.amax(plot_cmd2)+0.1, 0.25)
#ymin1 = min(np.amin(plot_y1)-0.1, np.amin(plot_cmd1)-0.1, np.amin(plot_cmd2)-0.1, -0.25)
#ymax2 = max(np.amax(plot_y2)+0.1, np.amax(plot_cmd1)+0.1, np.amax(plot_cmd2)+0.1, 0.25)
#ymin2 = min(np.amin(plot_y2)-0.1, np.amin(plot_cmd1)-0.1, np.amin(plot_cmd2)-0.1, -0.25)
ymax1 = max(np.amax(plot_y1)+0.1, np.amax(plot_cmd1)+0.1, 0.25)
ymin1 = min(np.amin(plot_y1)-0.1, np.amin(plot_cmd1)-0.1, -0.25)
ymax2 = max(np.amax(plot_y2)+0.1, np.amax(plot_cmd2)+0.1, 0.25)
ymin2 = min(np.amin(plot_y2)-0.1, np.amin(plot_cmd2)-0.1, -0.25)
fig1 = plt.figure()
plt.subplot(211)
plt.plot(plot_x, plot_y1, 'r--', label='Predicted Speed')
plt.plot(plot_x, plot_cmd1, 'k--', label='Left Wheel Command')
#plt.plot(plot_x, plot_cmd1, 'k--', label='Forward Command')
#plt.plot(plot_x, plot_cmd2, 'k-.', label='Left Command')
plt.ylim(ymin1, ymax1)
plt.title(title1)
#plt.legend(loc=2)
plt.subplot(212)
plt.plot(plot_x, plot_y2, 'r--', label='Predicted Speed')
plt.plot(plot_x, plot_cmd2, 'k--', label='Right Wheel Command')
#plt.plot(plot_x, plot_cmd1, 'k--', label='Forward Command')
#plt.plot(plot_x, plot_cmd2, 'k-.', label='Left Command')
plt.ylim(ymin2, ymax2)
plt.title(title2)
plt.legend(loc='center left', bbox_to_anchor=(1.0, 1.0))
fig1.savefig(filename, bbox_inches='tight', pad_inches=0)
pp.savefig()
plt.close()
pp.close()
def test_lwpr_5sec(datafile, resultfolder, model_epoch, hist_window, robot_type, cmd_scaler=1.0, modelfile='lwpr_model', saveFigure=False):
if (robot_type == 'Vulcan'):
plot_cmd_divider = 100.0
elif (robot_type == 'Fetch') or (robot_type == 'Fetch2') or (robot_type == 'MagicBot'):
plot_cmd_divider = 1.0
# load data file
dataset = loadmat(datafile)
test_data_x, test_data_y = dataset['test_data_x'], dataset['test_data_y']
num_test, test_dim = test_data_x.shape[0], test_data_y.shape[1]//2
speed_hw, cmd_hw = hist_window[0], hist_window[1]
input_dim = 2*(speed_hw+cmd_hw)
# normalize command part
test_data_x[:, 2*speed_hw:] = test_data_x[:, 2*speed_hw:] * cmd_scaler
# load model files
modelfile_name = './' + resultfolder + '/' + modelfile + '_best_left_epoch' + str(model_epoch) + '.bin'
best_left_model = LWPR(modelfile_name)
print 'Read Left model (%d)' % (best_left_model.num_rfs[0])
modelfile_name = './' + resultfolder + '/' + modelfile + '_best_right_epoch' + str(model_epoch) + '.bin'
best_right_model = LWPR(modelfile_name)
print 'Read Right model (%d)' % (best_right_model.num_rfs[0])
result_file_name = './' + resultfolder + '/Result_of_training_epoch' + str(model_epoch) + '.mat'
result_mat = loadmat(result_file_name)
train_time = result_mat['train_time']
hist_valid_error = result_mat['history_validation_error']
# start making 5 seconds simulation
pred_on_test = np.zeros((num_test, 2*test_dim))
tmp_x = np.zeros((input_dim, 1))
print 'start prediction on test data'
start_test_time = timeit.default_timer()
for data_cnt in range(num_test):
for pred_cnt in range(test_dim):
if pred_cnt < speed_hw:
num_value_from_data = 2*(speed_hw-pred_cnt)
tmp_x[0:num_value_from_data, 0] = test_data_x[data_cnt, 2*pred_cnt:2*speed_hw]
tmp_x[num_value_from_data:2*speed_hw, 0] = pred_on_test[data_cnt, 0:2*pred_cnt]
else:
tmp_x[0:2*speed_hw, 0] = pred_on_test[data_cnt, 2*(pred_cnt-speed_hw):2*pred_cnt]
tmp_x[2*speed_hw:input_dim, 0] = test_data_x[data_cnt, 2*(speed_hw+pred_cnt):2*(speed_hw+pred_cnt+cmd_hw)]
pred_on_test[data_cnt, 2*pred_cnt], _ = best_left_model.predict_conf(tmp_x)
pred_on_test[data_cnt, 2*pred_cnt+1], _ = best_right_model.predict_conf(tmp_x)
if data_cnt%5000 == 0:
print '\t\t', data_cnt
end_test_time = timeit.default_timer()
diff = abs(test_data_y[0:num_test, 0:2*test_dim] - pred_on_test)
max_index = diff.argmax() // (test_dim*2)
error = np.asarray([np.sum(diff)/float(num_test), np.sqrt(np.sum(diff**2)/float(num_test)), np.max(diff)])
print 'Error on Test Data! %f/%f/%f' %(error[0], error[1], error[2])
save_file_name = './' + resultfolder + '/LWPR_1D_model_test_result.mat'
result={}
result['train_time'] = train_time
result['test_time'] = end_test_time - start_test_time
result['history_validation_error'] = hist_valid_error
result['test_error'] = error
result['model_output_on_test_data'] = pred_on_test
result['joystick_command'] = test_data_x[:, (input_dim-2):(input_dim-2+250)]
savemat(save_file_name, result)
# plot (lots of) graphs
if saveFigure:
real_y_tmp, pred_y_tmp, joystick_cmd_tmp = test_data_y.reshape((num_test,125,2)), pred_on_test.reshape((num_test,125,2)), test_data_x[:, 2*(input_dim//2-1):2*(input_dim//2+124)].reshape((num_test,125,2))
real_y_left, real_y_right = np.zeros((num_test,125)), np.zeros((num_test,125))
pred_left_speed, pred_right_speed = np.zeros((num_test,125)), np.zeros((num_test,125))
forward_cmd, left_cmd = np.zeros((num_test,125)), np.zeros((num_test,125))
for cnt in range(num_test):
real_y_left[cnt,:] = real_y_tmp[cnt,:,0]
real_y_right[cnt,:] = real_y_tmp[cnt,:,1]
pred_left_speed[cnt,:] = pred_y_tmp[cnt,:,0]
pred_right_speed[cnt,:] = pred_y_tmp[cnt,:,1]
forward_cmd[cnt,:] = joystick_cmd_tmp[cnt,:,0]
left_cmd[cnt,:] = joystick_cmd_tmp[cnt,:,1]
plot_path = os.getcwd()
plot_path = plot_path + '/' + resultfolder
if not('ResultPlot' in os.listdir(plot_path)):
dir_path = plot_path + '/ResultPlot'
os.mkdir(dir_path)
pdf_name = plot_path + '/LWPR_test_plot.pdf'
pp = PdfPages(pdf_name)
for cnt in range(num_test // 25 + 1):
if cnt < num_test//25:
plot_cnt = cnt
else:
plot_cnt= max_index / 25.0
plot_x = np.linspace(plot_cnt, plot_cnt+5, num=125, endpoint=True)
plot_y1 = pred_left_speed[int(plot_cnt*25),:]
plot_y2 = pred_right_speed[int(plot_cnt*25),:]
plot_real_y1 = real_y_left[int(plot_cnt*25),:]
plot_real_y2 = real_y_right[int(plot_cnt*25),:]
plot_for_cmd = forward_cmd[int(plot_cnt*25),:]/plot_cmd_divider
plot_left_cmd = left_cmd[int(plot_cnt*25),:]/plot_cmd_divider
title1 = 'Left Wheel 5sec Prediction from ' + str(plot_cnt)
title2 = 'Right Wheel 5sec Prediction from ' + str(plot_cnt)
filename = plot_path + '/ResultPlot/5secLTS_' + str(plot_cnt) + '.png'
fig1 = plt.figure()
plt.subplot(211)
plt.plot(plot_x, plot_y1, 'r--', label='Predicted Speed')
plt.plot(plot_x, plot_real_y1, 'b-.', label='Encoded Speed')
if (robot_type == 'Vulcan'):
plt.plot(plot_x, plot_for_cmd, 'k--', label='Forward Command')
plt.plot(plot_x, plot_left_cmd, 'k-.', label='Left Command')
elif (robot_type == 'Fetch') or (robot_type == 'Fetch2') or (robot_type == 'MagicBot'):
plt.plot(plot_x, plot_for_cmd, 'k--', label='Left Command')
plt.title(title1)
#plt.legend(loc=2)
plt.subplot(212)
plt.plot(plot_x, plot_y2, 'r--', label='Predicted Speed')
plt.plot(plot_x, plot_real_y2, 'b-.', label='Encoded Speed')
if (robot_type == 'Vulcan'):
plt.plot(plot_x, plot_for_cmd, 'k--', label='Forward Command')
plt.plot(plot_x, plot_left_cmd, 'k-.', label='Left Command')
elif (robot_type == 'Fetch') or (robot_type == 'Fetch2') or (robot_type == 'MagicBot'):
plt.plot(plot_x, plot_left_cmd, 'k--', label='Right Command')
plt.title(title2)
plt.legend(loc='center left', bbox_to_anchor=(1.0, 1.0))
fig1.savefig(filename, bbox_inches='tight', pad_inches=0)
pp.savefig()
plt.close()
pp.close()
def train_lwpr(datafile, resultfolder, max_num_train, patience_list, improvement_threshold, init_lwpr_setting, hist_window, start_epoch=0, cmd_scaler=1.0, modelfile='lwpr_model'):
curr_path = os.getcwd()
if resultfolder in os.listdir(curr_path):
print "subfolder exists"
else:
print "Not Exist, so make subfolder"
os.mkdir(resultfolder)
# Load Data
dataset = loadmat(datafile)
train_data_x, train_data_y = dataset['train_data_x'], dataset['train_data_y']
valid_data_x, valid_data_y = dataset['valid_data_x'], dataset['valid_data_y']
num_data, num_valid = train_data_x.shape[0], valid_data_x.shape[0]
speed_hw, cmd_hw = hist_window[0], hist_window[1]
input_dim = 2*(speed_hw+cmd_hw)
# normalize command part
train_data_x[:, 2*speed_hw:] = train_data_x[:, 2*speed_hw:] * cmd_scaler
valid_data_x[:, 2*speed_hw:] = valid_data_x[:, 2*speed_hw:] * cmd_scaler
# Set-up Parameters/Model for Training Procedure
max_num_trials = max_num_train
improvement_threshold = improvement_threshold
error_hist, best_model_error, prev_train_time = [], np.inf, 0
initD, initA, penalty = init_lwpr_setting[0], init_lwpr_setting[1], init_lwpr_setting[2]
w_gen, w_prune = init_lwpr_setting[3], init_lwpr_setting[4]
best_model_epoch = 0
if start_epoch < 1:
# Initialize Two 1-Dimensional Models
LWPR_model_left = LWPR(input_dim, 1)
#LWPR_model_left.init_D = initD * np.eye(input_dim)
tmp_arr = np.ones(input_dim)
tmp_arr[input_dim-2*cmd_hw:input_dim] = init_lwpr_setting[5]
LWPR_model_left.init_D = initD * np.diag(tmp_arr)
LWPR_model_left.update_D = False # True
#LWPR_model_left.init_alpha = initA * np.eye(input_dim)
tmp_arr = np.ones(input_dim)
tmp_arr[input_dim-2*cmd_hw:input_dim] = init_lwpr_setting[5]
LWPR_model_left.init_alpha = initA * np.diag(tmp_arr)
LWPR_model_left.penalty = penalty
LWPR_model_left.meta = True
LWPR_model_left.meta_rate = 20
LWPR_model_left.w_gen = w_gen
LWPR_model_left.w_prune = w_prune
LWPR_model_right = LWPR(input_dim, 1)
#LWPR_model_right.init_D = initD * np.eye(input_dim)
tmp_arr = np.ones(input_dim)
tmp_arr[input_dim-2*cmd_hw:input_dim] = init_lwpr_setting[5]
LWPR_model_right.init_D = initD * np.diag(tmp_arr)
LWPR_model_right.update_D = False # True
#LWPR_model_right.init_alpha = initA * np.eye(input_dim)
tmp_arr = np.ones(input_dim)
tmp_arr[input_dim-2*cmd_hw:input_dim] = init_lwpr_setting[5]
LWPR_model_right.init_alpha = initA * np.diag(tmp_arr)
LWPR_model_right.penalty = penalty
LWPR_model_right.meta = True
LWPR_model_right.meta_rate = 20
LWPR_model_right.w_gen = w_gen
LWPR_model_right.w_prune = w_prune
patience = patience_list[0]
else:
modelfile_name = './' + resultfolder + '/' + modelfile + '_left_epoch' + str(start_epoch-1) + '.bin'
LWPR_model_left = LWPR(modelfile_name)
print '\tRead LWPR model for left wheel(%d)' % (LWPR_model_left.num_rfs[0])
modelfile_name = './' + resultfolder + '/' + modelfile + '_right_epoch' + str(start_epoch-1) + '.bin'
LWPR_model_right = LWPR(modelfile_name)
print '\tRead LWPR model for right wheel(%d)' % (LWPR_model_right.num_rfs[0])
result_file_name = './' + resultfolder + '/Result_of_training_epoch' + str(start_epoch-1) + '.mat'
result_file = loadmat(result_file_name)
prev_train_time = result_file['train_time']
patience = result_file['patience']
best_model_error = result_file['best_model_error']
for cnt in range(start_epoch):
error_hist.append([result_file['history_validation_error'][cnt][0], result_file['history_validation_error'][cnt][1], result_file['history_validation_error'][cnt][2]])
# Training Part
model_prediction = np.zeros(valid_data_y.shape)
tmp_x, tmp_y = np.zeros((input_dim, 1)), np.zeros((1,1))
print 'start training'
start_train_time = timeit.default_timer()
for train_cnt in range(start_epoch, max_num_trials):
if patience < train_cnt:
break
rand_ind = np.random.permutation(num_data)
for data_cnt in range(num_data):
tmp_x[:,0] = train_data_x[rand_ind[data_cnt], 0:input_dim]
tmp_y[0,0] = train_data_y[rand_ind[data_cnt], 0]
_ = LWPR_model_left.update(tmp_x, tmp_y)
tmp_y[0,0] = train_data_y[rand_ind[data_cnt], 1]
_ = LWPR_model_right.update(tmp_x, tmp_y)
if data_cnt % 5000 == 0:
print '\ttrain epoch %d, data index %d, #rfs=%d/%d' % (train_cnt, data_cnt, LWPR_model_left.num_rfs, LWPR_model_right.num_rfs)
for data_cnt in range(num_valid):
tmp_x[:,0] = valid_data_x[data_cnt, 0:input_dim]
model_prediction[data_cnt, 0], _ = LWPR_model_left.predict_conf(tmp_x)
model_prediction[data_cnt, 1], _ = LWPR_model_right.predict_conf(tmp_x)
diff = abs(valid_data_y - model_prediction)
new_error = np.asarray([np.sum(diff)/float(num_valid), np.sqrt(np.sum(diff**2)/float(num_valid)), np.max(diff)])
error_hist.append([new_error[0], new_error[1], new_error[2]])
# save result of one training epoch
modelfile_name = './' + resultfolder + '/' + modelfile + '_left_epoch' + str(train_cnt) + '.bin'
LWPR_model_left.write_binary(modelfile_name)
modelfile_name = './' + resultfolder + '/' + modelfile + '_right_epoch' + str(train_cnt) + '.bin'
LWPR_model_right.write_binary(modelfile_name)
if new_error[1] < best_model_error * improvement_threshold:
best_model_epoch = train_cnt
best_model_error = new_error[1]
patience = max(patience, min(train_cnt+10, int(train_cnt * patience_list[1])) )
modelfile_name = './' + resultfolder + '/' + modelfile + '_best_left_epoch' + str(train_cnt) + '.bin'
LWPR_model_left.write_binary(modelfile_name)
modelfile_name = './' + resultfolder + '/' + modelfile + '_best_right_epoch' + str(train_cnt) + '.bin'
LWPR_model_right.write_binary(modelfile_name)
result_file_name = './' + resultfolder + '/Result_of_training_epoch' + str(train_cnt) + '.mat'
result = {}
result['train_time'] = timeit.default_timer() - start_train_time + prev_train_time
result['best_model_error'] = best_model_error
result['history_validation_error'] = error_hist
result['patience'] = patience
result['improvement_threshold'] = improvement_threshold
result['init_D'] = initD
result['init_alpha'] = initA
result['penalty'] = penalty
result['w_generate_criterion'] = w_gen
result['w_prune_criterion'] = w_prune
result['number_speed_in_input'] = 2*speed_hw
result['number_cmd_in_input'] = 2*cmd_hw
savemat(result_file_name, result)
print '\n\tSave Intermediate Result Successfully'
print '\t%d-th learning : #Data=%d/%d, #rfs=%d/%d, error=%f\n' %(train_cnt, LWPR_model_left.n_data, LWPR_model_right.n_data, LWPR_model_left.num_rfs, LWPR_model_right.num_rfs, error_hist[train_cnt][1])
print 'end training'
return best_model_epoch
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--environment", type=str, default='AntBulletEnv-v0')
parser.add_argument("--no_data_start", type=int, default=10000)
parser.add_argument("--train_policy_batch_size", type=int, default=30)
parser.add_argument("--cma_maxiter", type=int, default=1000)
parser.add_argument("--unroll_steps", type=int, default=200)
args = parser.parse_args()
print args
env = gym.make(args.environment)
input_dim = env.observation_space.shape[0]+env.action_space.shape[0]
output_dim = env.observation_space.shape[0] + 1
model = LWPR(input_dim, output_dim)
model.init_D = 1. * np.eye(input_dim)
model.update_D = True
model.init_alpha = 20. * np.eye(input_dim)
model.meta = True
agent = AGENT(env.observation_space.shape[0],
env.action_space.shape[0],
action_space_low=env.action_space.low,
action_space_high=env.action_space.high,
unroll_steps=args.unroll_steps)
init_states = np.stack([env.reset() for _ in range(args.train_policy_batch_size)], axis=0)
#Train the dynamics model the intial data.
data_buffer = gather_data3(env, args.no_data_start)
states, actions, rewards, next_states, _ = zip(*data_buffer)
states = np.stack(states, axis=0)
actions = np.stack(actions, axis=0)
rewards = np.array(rewards)[..., np.newaxis]
next_states = np.stack(next_states, axis=0)
state_actions = np.concatenate([states, actions], axis=-1)
state_diff = next_states - states
targets = np.concatenate([state_diff, rewards], axis=-1)
assert len(state_actions) == len(targets)
ind = np.random.permutation(len(state_actions))
for i in range(len(state_actions)):
model.update(state_actions[ind[i]], targets[ind[i]])
for epoch in range(1000):
agent._fit(model, init_states, args.cma_maxiter)
total_rewards = 0.
state = env.reset()
while True:
action = agent._forward(agent.thetas, state[np.newaxis, ...])[0]
next_state, reward, done, _ = env.step(action)
state_action = np.concatenate([state, action])
state_diff = next_state - state
target = np.append(state_diff, reward)
model.update(state_action, target)
total_rewards += float(reward)
state = next_state.copy()
if done:
print 'epoch:', epoch, 'total_rewards:', total_rewards
break
context1 = [2.0, context[0]]
context2 = [3.6, context[1]]
circle1 = plt.Circle((context1[0], context1[1]), 0.1, color='b', fill=False)
circle2 = plt.Circle((context2[0], context2[1]), 0.1, color='b', fill=False)
ax = plt.gca()
ax.add_artist(circle1)
ax.add_artist(circle2)
plt.grid()
plt.savefig('/home/fmeccanici/Documents/thesis/thesis_workspace/src/promp_demo_2d/figures/lwpr/lwpr_demos.png')
plt.clf()
# initialize lwpr model
model = LWPR(n_in, n_out)
model.init_D = 10*eye(n_in)
model.init_alpha = 0.1* eye(n_in)
# model.kernel = 'BiSquare'
for i in range(10):
for demonstration in demonstrations:
output = np.asarray(demonstration[0])
context = np.asarray(demonstration[1])
# print("added output: " + str(output))
# print("added context: " + str(context))
model.update(context, output)
# generalize
# Copied from http://www.rueckstiess.net/research/snippets/show/9bd4b418
from numpy import *
from matplotlib import pyplot as plt
from lwpr import LWPR
def testfunc(x):
return 10 * sin(7.8 * log(1 + x)) / (1 + 0.1 * x**2)
Ntr = 500
Xtr = 10 * random.random((Ntr, 1))
Ytr = 5 + testfunc(Xtr) + 0.1 * random.normal(0, 1, (Ntr, 1)) * Xtr
# initialize the LWPR model
model = LWPR(1, 1)
model.init_D = 20 * eye(1)
model.update_D = True
model.init_alpha = 40 * eye(1)
model.meta = False
model.penalty = 1e-4
model.diag_only = True
# train the model
for k in range(20):
ind = random.permutation(Ntr)
mse = 0
for i in range(Ntr):
yp = model.update(Xtr[ind[i]], Ytr[ind[i]])
mse = mse + (Ytr[ind[i], :] - yp)**2