class GPSTrainingGUI(object):
def __init__(self, hyperparams):
self._hyperparams = hyperparams
self._log_filename = self._hyperparams['log_filename']
if 'target_filename' in self._hyperparams:
self._target_filename = self._hyperparams['target_filename']
else:
self._target_filename = None
# GPS Training Status.
self.mode = config['initial_mode'] # Modes: run, wait, end, request, process.
self.request = None # Requests: stop, reset, go, fail, None.
self.err_msg = None
self._colors = {
'run': 'cyan',
'wait': 'orange',
'end': 'red',
'stop': 'red',
'reset': 'yellow',
'go': 'green',
'fail': 'magenta',
}
self._first_update = True
# Actions.
actions_arr = [
Action('stop', 'stop', self.request_stop, axis_pos=0),
Action('reset', 'reset', self.request_reset, axis_pos=1),
Action('go', 'go', self.request_go, axis_pos=2),
Action('fail', 'fail', self.request_fail, axis_pos=3),
]
# Setup figure.
plt.ion()
plt.rcParams['toolbar'] = 'None'
for key in plt.rcParams:
if key.startswith('keymap.'):
plt.rcParams[key] = ''
self._fig = plt.figure(figsize=config['figsize'])
self._fig.subplots_adjust(left=0.01, bottom=0.01, right=0.99, top=0.99,
wspace=0, hspace=0)
# Assign GUI component locations.
self._gs = gridspec.GridSpec(16, 8)
self._gs_action_panel = self._gs[0:1, 0:8]
self._gs_action_output = self._gs[1:2, 0:4]
self._gs_status_output = self._gs[2:3, 0:4]
self._gs_cost_plotter = self._gs[1:3, 4:8]
self._gs_gt_cost_plotter = self._gs[4:6, 4:8]
self._gs_algthm_output = self._gs[3:9, 0:4]
if config['image_on']:
self._gs_traj_visualizer = self._gs[9:16, 0:4]
self._gs_image_visualizer = self._gs[9:16, 4:8]
else:
self._gs_traj_visualizer = self._gs[9:16, 0:8]
# Create GUI components.
self._action_panel = ActionPanel(self._fig, self._gs_action_panel, 1, 4, actions_arr)
self._action_output = Textbox(self._fig, self._gs_action_output, border_on=True)
self._status_output = Textbox(self._fig, self._gs_status_output, border_on=False)
self._algthm_output = Textbox(self._fig, self._gs_algthm_output,
max_display_size=config['algthm_output_max_display_size'],
log_filename=self._log_filename,
fontsize=config['algthm_output_fontsize'],
font_family='monospace')
self._cost_plotter = MeanPlotter(self._fig, self._gs_cost_plotter,
color='blue', label='mean cost')
self._gt_cost_plotter = MeanPlotter(self._fig, self._gs_gt_cost_plotter,
color='red', label='ground truth cost')
self._traj_visualizer = Plotter3D(self._fig, self._gs_traj_visualizer,
num_plots=self._hyperparams['conditions'])
if config['image_on']:
self._image_visualizer = ImageVisualizer(self._fig,
self._gs_image_visualizer, cropsize=config['image_size'],
rostopic=config['image_topic'], show_overlay_buttons=True)
# Setup GUI components.
self._algthm_output.log_text('\n')
self.set_output_text(self._hyperparams['info'])
if config['initial_mode'] == 'run':
self.run_mode()
else:
self.wait_mode()
# Setup 3D Trajectory Visualizer plot titles and legends
for m in range(self._hyperparams['conditions']):
self._traj_visualizer.set_title(m, 'Condition %d' % (m))
self._traj_visualizer.add_legend(linestyle='-', marker='None',
color='green', label='Trajectory Samples')
self._traj_visualizer.add_legend(linestyle='-', marker='None',
color='blue', label='Policy Samples')
self._traj_visualizer.add_legend(linestyle='None', marker='x',
color=(0.5, 0, 0), label='LG Controller Means')
self._traj_visualizer.add_legend(linestyle='-', marker='None',
color='red', label='LG Controller Distributions')
self._fig.canvas.draw()
# Display calculating thread
def display_calculating(delay, run_event):
while True:
if not run_event.is_set():
run_event.wait()
if run_event.is_set():
self.set_status_text('Calculating.')
time.sleep(delay)
if run_event.is_set():
self.set_status_text('Calculating..')
time.sleep(delay)
if run_event.is_set():
self.set_status_text('Calculating...')
time.sleep(delay)
self._calculating_run = threading.Event()
self._calculating_thread = threading.Thread(target=display_calculating,
args=(1, self._calculating_run))
self._calculating_thread.daemon = True
self._calculating_thread.start()
# GPS Training functions
def request_stop(self, event=None):
self.request_mode('stop')
def request_reset(self, event=None):
self.request_mode('reset')
def request_go(self, event=None):
self.request_mode('go')
def request_fail(self, event=None):
self.request_mode('fail')
def request_mode(self, request):
"""
Sets the request mode (stop, reset, go, fail). The request is read by
gps_main before sampling, and the appropriate action is taken.
"""
self.mode = 'request'
self.request = request
self.set_action_text(self.request + ' requested')
self.set_action_bgcolor(self._colors[self.request], alpha=0.2)
def process_mode(self):
"""
Completes the current request, after it is first read by gps_main.
Displays visual confirmation that the request was processed,
displays any error messages, and then switches into mode 'run' or 'wait'.
"""
self.mode = 'process'
self.set_action_text(self.request + ' processed')
self.set_action_bgcolor(self._colors[self.request], alpha=1.0)
if self.err_msg:
self.set_action_text(self.request + ' processed' + '\nERROR: ' +
self.err_msg)
self.err_msg = None
time.sleep(1.0)
else:
time.sleep(0.5)
if self.request in ('stop', 'reset', 'fail'):
self.wait_mode()
elif self.request == 'go':
self.run_mode()
self.request = None
def wait_mode(self):
self.mode = 'wait'
self.set_action_text('waiting')
self.set_action_bgcolor(self._colors[self.mode], alpha=1.0)
def run_mode(self):
self.mode = 'run'
self.set_action_text('running')
self.set_action_bgcolor(self._colors[self.mode], alpha=1.0)
def end_mode(self):
self.mode = 'end'
self.set_action_text('ended')
self.set_action_bgcolor(self._colors[self.mode], alpha=1.0)
def estop(self, event=None):
self.set_action_text('estop: NOT IMPLEMENTED')
# GUI functions
def set_action_text(self, text):
self._action_output.set_text(text)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
self._gt_cost_plotter.draw_ticklabels()
def set_action_bgcolor(self, color, alpha=1.0):
self._action_output.set_bgcolor(color, alpha)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
self._gt_cost_plotter.draw_ticklabels()
def set_status_text(self, text):
self._status_output.set_text(text)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
self._gt_cost_plotter.draw_ticklabels()
def set_output_text(self, text):
self._algthm_output.set_text(text)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
self._gt_cost_plotter.draw_ticklabels()
def append_output_text(self, text):
self._algthm_output.append_text(text)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
self._gt_cost_plotter.draw_ticklabels()
def start_display_calculating(self):
self._calculating_run.set()
def stop_display_calculating(self):
self._calculating_run.clear()
def set_image_overlays(self, condition):
"""
Sets up the image visualizer with what images to overlay if
"overlay_initial_image" or "overlay_target_image" is pressed.
"""
if not config['image_on'] or not self._target_filename:
return
initial_image = load_data_from_npz(self._target_filename,
config['image_overlay_actuator'], str(condition),
'initial', 'image', default=None)
target_image = load_data_from_npz(self._target_filename,
config['image_overlay_actuator'], str(condition),
'target', 'image', default=None)
self._image_visualizer.set_initial_image(initial_image,
alpha=config['image_overlay_alpha'])
self._image_visualizer.set_target_image(target_image,
alpha=config['image_overlay_alpha'])
# Iteration update functions
def update(self, itr, algorithm, agent, traj_sample_lists, pol_sample_lists):
"""
After each iteration, update the iteration data output, the cost plot,
and the 3D trajectory visualizations (if end effector points exist).
"""
if self._first_update:
policy_titles = pol_sample_lists != None
self._output_column_titles(algorithm, policy_titles)
self._first_update = False
costs = [np.mean(np.sum(algorithm.prev[m].cs, axis=1)) for m in range(algorithm.M)]
if algorithm._hyperparams['ioc']:
gt_costs = [np.mean(np.sum(algorithm.prev[m].cgt, axis=1)) for m in range(algorithm.M)]
self._update_iteration_data(itr, algorithm, gt_costs, pol_sample_lists)
self._gt_cost_plotter.update(gt_costs, t=itr)
else:
self._update_iteration_data(itr, algorithm, costs, pol_sample_lists)
self._cost_plotter.update(costs, t=itr)
if END_EFFECTOR_POINTS in agent.x_data_types:
self._update_trajectory_visualizations(algorithm, agent,
traj_sample_lists, pol_sample_lists)
self._fig.canvas.draw()
self._fig.canvas.flush_events() # Fixes bug in Qt4Agg backend
# import pdb; pdb.set_trace()
def _output_column_titles(self, algorithm, policy_titles=False):
"""
Setup iteration data column titles: iteration, average cost, and for
each condition the mean cost over samples, step size, linear Guassian
controller entropies, and initial/final KL divergences for BADMM.
"""
self.set_output_text(self._hyperparams['experiment_name'])
if policy_titles:
condition_titles = '%3s | %8s %12s' % ('', '', '')
itr_data_fields = '%3s | %8s %12s' % ('itr', 'avg_cost', 'avg_pol_cost')
else:
condition_titles = '%3s | %8s' % ('', '')
itr_data_fields = '%3s | %8s' % ('itr', 'avg_cost')
for m in range(algorithm.M):
condition_titles += ' | %8s %9s %-7d' % ('', 'condition', m)
itr_data_fields += ' | %8s %8s %8s' % (' cost ', ' step ', 'entropy ')
if algorithm.prev[0].pol_info is not None:
condition_titles += ' %8s %8s' % ('', '')
itr_data_fields += ' %8s %8s' % ('kl_div_i', 'kl_div_f')
if algorithm._hyperparams['ioc'] and not algorithm._hyperparams['learning_from_prior']:
condition_titles += ' %8s' % ('')
itr_data_fields += ' %8s' % ('kl_div')
if algorithm._hyperparams['learning_from_prior']:
condition_titles += ' %8s' % ('')
itr_data_fields += ' %8s' % ('mean_dist')
if policy_titles:
condition_titles += ' %8s %8s %8s' % ('', '', '')
itr_data_fields += ' %8s %8s %8s' % ('pol_cost', 'kl_div_i', 'kl_div_f')
self.append_output_text(condition_titles)
self.append_output_text(itr_data_fields)
def _update_iteration_data(self, itr, algorithm, costs, pol_sample_lists):
"""
Update iteration data information: iteration, average cost, and for
each condition the mean cost over samples, step size, linear Guassian
controller entropies, and initial/final KL divergences for BADMM.
"""
avg_cost = np.mean(costs)
if pol_sample_lists is not None:
pol_costs = [np.mean([np.sum(algorithm.cost[m].eval(s)[0]) \
for s in pol_sample_lists[m]]) \
for m in range(algorithm.M)]
itr_data = '%3d | %8.2f %12.2f' % (itr, avg_cost, np.mean(pol_costs))
else:
itr_data = '%3d | %8.2f' % (itr, avg_cost)
for m in range(algorithm.M):
cost = costs[m]
step = algorithm.prev[m].step_mult * algorithm.base_kl_step
entropy = 2*np.sum(np.log(np.diagonal(algorithm.prev[m].traj_distr.chol_pol_covar,
axis1=1, axis2=2)))
itr_data += ' | %8.2f %8.2f %8.2f' % (cost, step, entropy)
if algorithm.prev[0].pol_info is not None:
kl_div_i = algorithm.prev[m].pol_info.prev_kl[0]
kl_div_f = algorithm.prev[m].pol_info.prev_kl[-1]
itr_data += ' %8.2f %8.2f' % (kl_div_i, kl_div_f)
if algorithm._hyperparams['ioc'] and not algorithm._hyperparams['learning_from_prior']:
itr_data += ' %8.2f' % (algorithm.kl_div[itr][m])
if algorithm._hyperparams['learning_from_prior']:
itr_data += ' %8.2f' % (algorithm.dists_to_target[itr][m])
if pol_sample_lists is not None:
kl_div_i = algorithm.cur[m].pol_info.init_kl.mean()
kl_div_f = algorithm.cur[m].pol_info.prev_kl.mean()
itr_data += ' %8.2f %8.2f %8.2f' % (pol_costs[m], kl_div_i, kl_div_f)
self.append_output_text(itr_data)
def _update_trajectory_visualizations(self, algorithm, agent,
traj_sample_lists, pol_sample_lists):
"""
Update 3D trajectory visualizations information: the trajectory samples,
policy samples, and linear Gaussian controller means and covariances.
"""
xlim, ylim, zlim = self._calculate_3d_axis_limits(traj_sample_lists, pol_sample_lists)
for m in range(algorithm.M):
self._traj_visualizer.clear(m)
self._traj_visualizer.set_lim(i=m, xlim=xlim, ylim=ylim, zlim=zlim)
self._update_samples_plots(traj_sample_lists, m, 'green', 'Trajectory Samples')
self._update_linear_gaussian_controller_plots(algorithm, agent, m)
if pol_sample_lists:
self._update_samples_plots(pol_sample_lists, m, 'blue', 'Policy Samples')
self._traj_visualizer.draw() # this must be called explicitly
def _calculate_3d_axis_limits(self, traj_sample_lists, pol_sample_lists):
"""
Calculate the 3D axis limits shared between trajectory plots,
based on the minimum and maximum xyz values across all samples.
"""
all_eept = np.empty((0, 3))
sample_lists = traj_sample_lists
if pol_sample_lists:
sample_lists += traj_sample_lists
for sample_list in sample_lists:
for sample in sample_list.get_samples():
ee_pt = sample.get(END_EFFECTOR_POINTS)
for i in range(ee_pt.shape[1]/3):
ee_pt_i = ee_pt[:, 3*i+0:3*i+3]
all_eept = np.r_[all_eept, ee_pt_i]
min_xyz = np.amin(all_eept, axis=0)
max_xyz = np.amax(all_eept, axis=0)
xlim = buffered_axis_limits(min_xyz[0], max_xyz[0], buffer_factor=1.25)
ylim = buffered_axis_limits(min_xyz[1], max_xyz[1], buffer_factor=1.25)
zlim = buffered_axis_limits(min_xyz[2], max_xyz[2], buffer_factor=1.25)
return xlim, ylim, zlim
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_samples_plots(self, sample_lists, m, color, label):
"""
Update the samples plots with iteration data, for the trajectory samples
and the policy samples.
"""
samples = sample_lists[m].get_samples()
for sample in samples:
ee_pt = sample.get(END_EFFECTOR_POINTS)
for i in range(ee_pt.shape[1]/3):
ee_pt_i = ee_pt[:, 3*i+0:3*i+3]
self._traj_visualizer.plot_3d_points(m, ee_pt_i, color=color, label=label)
def save_figure(self, filename):
self._fig.savefig(filename)
class GPSTrainingGUI(object):
def __init__(self, hyperparams):
self._hyperparams = hyperparams
self._log_filename = self._hyperparams['log_filename']
if 'target_filename' in self._hyperparams:
self._target_filename = self._hyperparams['target_filename']
else:
self._target_filename = None
# GPS Training Status.
self.mode = config[
'initial_mode'] # Modes: run, wait, end, request, process.
self.request = None # Requests: stop, reset, go, fail, None.
self.err_msg = None
self._colors = {
'run': 'cyan',
'wait': 'orange',
'end': 'red',
'stop': 'red',
'reset': 'yellow',
'go': 'green',
'fail': 'magenta',
}
self._first_update = True
# Actions.
actions_arr = [
Action('stop', 'stop', self.request_stop, axis_pos=0),
Action('reset', 'reset', self.request_reset, axis_pos=1),
Action('go', 'go', self.request_go, axis_pos=2),
Action('fail', 'fail', self.request_fail, axis_pos=3),
]
# Setup figure.
plt.ion()
plt.rcParams['toolbar'] = 'None'
for key in plt.rcParams:
if key.startswith('keymap.'):
plt.rcParams[key] = ''
self._fig = plt.figure(figsize=config['figsize'])
self._fig.subplots_adjust(left=0.01,
bottom=0.01,
right=0.99,
top=0.99,
wspace=0,
hspace=0)
# Assign GUI component locations.
self._gs = gridspec.GridSpec(16, 8)
self._gs_action_panel = self._gs[0:1, 0:8]
self._gs_action_output = self._gs[1:2, 0:4]
self._gs_status_output = self._gs[2:3, 0:4]
self._gs_cost_plotter = self._gs[1:3, 4:8]
self._gs_gt_cost_plotter = self._gs[4:6, 4:8]
self._gs_algthm_output = self._gs[3:9, 0:4]
if config['image_on']:
self._gs_traj_visualizer = self._gs[9:16, 0:4]
self._gs_image_visualizer = self._gs[9:16, 4:8]
else:
self._gs_traj_visualizer = self._gs[9:16, 0:8]
# Create GUI components.
self._action_panel = ActionPanel(self._fig, self._gs_action_panel, 1,
4, actions_arr)
self._action_output = Textbox(self._fig,
self._gs_action_output,
border_on=True)
self._status_output = Textbox(self._fig,
self._gs_status_output,
border_on=False)
self._algthm_output = Textbox(
self._fig,
self._gs_algthm_output,
max_display_size=config['algthm_output_max_display_size'],
log_filename=self._log_filename,
fontsize=config['algthm_output_fontsize'],
font_family='monospace')
self._cost_plotter = MeanPlotter(self._fig,
self._gs_cost_plotter,
color='blue',
label='mean cost')
self._gt_cost_plotter = MeanPlotter(self._fig,
self._gs_gt_cost_plotter,
color='red',
label='ground truth cost')
self._traj_visualizer = Plotter3D(
self._fig,
self._gs_traj_visualizer,
num_plots=self._hyperparams['conditions'])
if config['image_on']:
self._image_visualizer = ImageVisualizer(
self._fig,
self._gs_image_visualizer,
cropsize=config['image_size'],
rostopic=config['image_topic'],
show_overlay_buttons=True)
# Setup GUI components.
self._algthm_output.log_text('\n')
self.set_output_text(self._hyperparams['info'])
if config['initial_mode'] == 'run':
self.run_mode()
else:
self.wait_mode()
# Setup 3D Trajectory Visualizer plot titles and legends
for m in range(self._hyperparams['conditions']):
self._traj_visualizer.set_title(m, 'Condition %d' % (m))
self._traj_visualizer.add_legend(linestyle='-',
marker='None',
color='green',
label='Trajectory Samples')
self._traj_visualizer.add_legend(linestyle='-',
marker='None',
color='blue',
label='Policy Samples')
self._traj_visualizer.add_legend(linestyle='None',
marker='x',
color=(0.5, 0, 0),
label='LG Controller Means')
self._traj_visualizer.add_legend(linestyle='-',
marker='None',
color='red',
label='LG Controller Distributions')
self._fig.canvas.draw()
# Display calculating thread
def display_calculating(delay, run_event):
while True:
if not run_event.is_set():
run_event.wait()
if run_event.is_set():
self.set_status_text('Calculating.')
time.sleep(delay)
if run_event.is_set():
self.set_status_text('Calculating..')
time.sleep(delay)
if run_event.is_set():
self.set_status_text('Calculating...')
time.sleep(delay)
self._calculating_run = threading.Event()
self._calculating_thread = threading.Thread(
target=display_calculating, args=(1, self._calculating_run))
self._calculating_thread.daemon = True
self._calculating_thread.start()
# GPS Training functions
def request_stop(self, event=None):
self.request_mode('stop')
def request_reset(self, event=None):
self.request_mode('reset')
def request_go(self, event=None):
self.request_mode('go')
def request_fail(self, event=None):
self.request_mode('fail')
def request_mode(self, request):
"""
Sets the request mode (stop, reset, go, fail). The request is read by
gps_main before sampling, and the appropriate action is taken.
"""
self.mode = 'request'
self.request = request
self.set_action_text(self.request + ' requested')
self.set_action_bgcolor(self._colors[self.request], alpha=0.2)
def process_mode(self):
"""
Completes the current request, after it is first read by gps_main.
Displays visual confirmation that the request was processed,
displays any error messages, and then switches into mode 'run' or 'wait'.
"""
self.mode = 'process'
self.set_action_text(self.request + ' processed')
self.set_action_bgcolor(self._colors[self.request], alpha=1.0)
if self.err_msg:
self.set_action_text(self.request + ' processed' + '\nERROR: ' +
self.err_msg)
self.err_msg = None
time.sleep(1.0)
else:
time.sleep(0.5)
if self.request in ('stop', 'reset', 'fail'):
self.wait_mode()
elif self.request == 'go':
self.run_mode()
self.request = None
def wait_mode(self):
self.mode = 'wait'
self.set_action_text('waiting')
self.set_action_bgcolor(self._colors[self.mode], alpha=1.0)
def run_mode(self):
self.mode = 'run'
self.set_action_text('running')
self.set_action_bgcolor(self._colors[self.mode], alpha=1.0)
def end_mode(self):
self.mode = 'end'
self.set_action_text('ended')
self.set_action_bgcolor(self._colors[self.mode], alpha=1.0)
def estop(self, event=None):
self.set_action_text('estop: NOT IMPLEMENTED')
# GUI functions
def set_action_text(self, text):
self._action_output.set_text(text)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
self._gt_cost_plotter.draw_ticklabels()
def set_action_bgcolor(self, color, alpha=1.0):
self._action_output.set_bgcolor(color, alpha)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
self._gt_cost_plotter.draw_ticklabels()
def set_status_text(self, text):
self._status_output.set_text(text)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
self._gt_cost_plotter.draw_ticklabels()
def set_output_text(self, text):
self._algthm_output.set_text(text)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
self._gt_cost_plotter.draw_ticklabels()
def append_output_text(self, text):
self._algthm_output.append_text(text)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
self._gt_cost_plotter.draw_ticklabels()
def start_display_calculating(self):
self._calculating_run.set()
def stop_display_calculating(self):
self._calculating_run.clear()
def set_image_overlays(self, condition):
"""
Sets up the image visualizer with what images to overlay if
"overlay_initial_image" or "overlay_target_image" is pressed.
"""
if not config['image_on'] or not self._target_filename:
return
initial_image = load_data_from_npz(self._target_filename,
config['image_overlay_actuator'],
str(condition),
'initial',
'image',
default=None)
target_image = load_data_from_npz(self._target_filename,
config['image_overlay_actuator'],
str(condition),
'target',
'image',
default=None)
self._image_visualizer.set_initial_image(
initial_image, alpha=config['image_overlay_alpha'])
self._image_visualizer.set_target_image(
target_image, alpha=config['image_overlay_alpha'])
# Iteration update functions
def update(self, itr, algorithm, agent, traj_sample_lists,
pol_sample_lists):
"""
After each iteration, update the iteration data output, the cost plot,
and the 3D trajectory visualizations (if end effector points exist).
"""
if self._first_update:
policy_titles = pol_sample_lists != None
self._output_column_titles(algorithm, policy_titles)
self._first_update = False
costs = [
np.mean(np.sum(algorithm.prev[m].cs, axis=1))
for m in range(algorithm.M)
]
if algorithm._hyperparams['ioc']:
gt_costs = [
np.mean(np.sum(algorithm.prev[m].cgt, axis=1))
for m in range(algorithm.M)
]
self._update_iteration_data(itr, algorithm, gt_costs,
pol_sample_lists)
self._gt_cost_plotter.update(gt_costs, t=itr)
else:
self._update_iteration_data(itr, algorithm, costs,
pol_sample_lists)
self._cost_plotter.update(costs, t=itr)
if END_EFFECTOR_POINTS in agent.x_data_types:
self._update_trajectory_visualizations(algorithm, agent,
traj_sample_lists,
pol_sample_lists)
self._fig.canvas.draw()
self._fig.canvas.flush_events() # Fixes bug in Qt4Agg backend
# import pdb; pdb.set_trace()
def _output_column_titles(self, algorithm, policy_titles=False):
"""
Setup iteration data column titles: iteration, average cost, and for
each condition the mean cost over samples, step size, linear Guassian
controller entropies, and initial/final KL divergences for BADMM.
"""
self.set_output_text(self._hyperparams['experiment_name'])
if policy_titles:
condition_titles = '%3s | %8s %12s' % ('', '', '')
itr_data_fields = '%3s | %8s %12s' % ('itr', 'avg_cost',
'avg_pol_cost')
else:
condition_titles = '%3s | %8s' % ('', '')
itr_data_fields = '%3s | %8s' % ('itr', 'avg_cost')
for m in range(algorithm.M):
condition_titles += ' | %8s %9s %-7d' % ('', 'condition', m)
itr_data_fields += ' | %8s %8s %8s' % (' cost ', ' step ',
'entropy ')
if algorithm.prev[0].pol_info is not None:
condition_titles += ' %8s %8s' % ('', '')
itr_data_fields += ' %8s %8s' % ('kl_div_i', 'kl_div_f')
if algorithm._hyperparams['ioc'] and not algorithm._hyperparams[
'learning_from_prior']:
condition_titles += ' %8s' % ('')
itr_data_fields += ' %8s' % ('kl_div')
if algorithm._hyperparams['learning_from_prior']:
condition_titles += ' %8s' % ('')
itr_data_fields += ' %8s' % ('mean_dist')
if policy_titles:
condition_titles += ' %8s %8s %8s' % ('', '', '')
itr_data_fields += ' %8s %8s %8s' % ('pol_cost', 'kl_div_i',
'kl_div_f')
self.append_output_text(condition_titles)
self.append_output_text(itr_data_fields)
def _update_iteration_data(self, itr, algorithm, costs, pol_sample_lists):
"""
Update iteration data information: iteration, average cost, and for
each condition the mean cost over samples, step size, linear Guassian
controller entropies, and initial/final KL divergences for BADMM.
"""
avg_cost = np.mean(costs)
if pol_sample_lists is not None:
pol_costs = [np.mean([np.sum(algorithm.cost[m].eval(s)[0]) \
for s in pol_sample_lists[m]]) \
for m in range(algorithm.M)]
itr_data = '%3d | %8.2f %12.2f' % (itr, avg_cost,
np.mean(pol_costs))
else:
itr_data = '%3d | %8.2f' % (itr, avg_cost)
for m in range(algorithm.M):
cost = costs[m]
step = algorithm.prev[m].step_mult * algorithm.base_kl_step
entropy = 2 * np.sum(
np.log(
np.diagonal(algorithm.prev[m].traj_distr.chol_pol_covar,
axis1=1,
axis2=2)))
itr_data += ' | %8.2f %8.2f %8.2f' % (cost, step, entropy)
if algorithm.prev[0].pol_info is not None:
kl_div_i = algorithm.prev[m].pol_info.prev_kl[0]
kl_div_f = algorithm.prev[m].pol_info.prev_kl[-1]
itr_data += ' %8.2f %8.2f' % (kl_div_i, kl_div_f)
if algorithm._hyperparams['ioc'] and not algorithm._hyperparams[
'learning_from_prior']:
itr_data += ' %8.2f' % (algorithm.kl_div[itr][m])
if algorithm._hyperparams['learning_from_prior']:
itr_data += ' %8.2f' % (algorithm.dists_to_target[itr][m])
if pol_sample_lists is not None:
kl_div_i = algorithm.cur[m].pol_info.init_kl.mean()
kl_div_f = algorithm.cur[m].pol_info.prev_kl.mean()
itr_data += ' %8.2f %8.2f %8.2f' % (pol_costs[m], kl_div_i,
kl_div_f)
self.append_output_text(itr_data)
def _update_trajectory_visualizations(self, algorithm, agent,
traj_sample_lists, pol_sample_lists):
"""
Update 3D trajectory visualizations information: the trajectory samples,
policy samples, and linear Gaussian controller means and covariances.
"""
xlim, ylim, zlim = self._calculate_3d_axis_limits(
traj_sample_lists, pol_sample_lists)
for m in range(algorithm.M):
self._traj_visualizer.clear(m)
self._traj_visualizer.set_lim(i=m, xlim=xlim, ylim=ylim, zlim=zlim)
self._update_samples_plots(traj_sample_lists, m, 'green',
'Trajectory Samples')
self._update_linear_gaussian_controller_plots(algorithm, agent, m)
if pol_sample_lists:
self._update_samples_plots(pol_sample_lists, m, 'blue',
'Policy Samples')
self._traj_visualizer.draw() # this must be called explicitly
def _calculate_3d_axis_limits(self, traj_sample_lists, pol_sample_lists):
"""
Calculate the 3D axis limits shared between trajectory plots,
based on the minimum and maximum xyz values across all samples.
"""
all_eept = np.empty((0, 3))
sample_lists = traj_sample_lists
if pol_sample_lists:
sample_lists += traj_sample_lists
for sample_list in sample_lists:
for sample in sample_list.get_samples():
ee_pt = sample.get(END_EFFECTOR_POINTS)
for i in range(ee_pt.shape[1] / 3):
ee_pt_i = ee_pt[:, 3 * i + 0:3 * i + 3]
all_eept = np.r_[all_eept, ee_pt_i]
min_xyz = np.amin(all_eept, axis=0)
max_xyz = np.amax(all_eept, axis=0)
xlim = buffered_axis_limits(min_xyz[0], max_xyz[0], buffer_factor=1.25)
ylim = buffered_axis_limits(min_xyz[1], max_xyz[1], buffer_factor=1.25)
zlim = buffered_axis_limits(min_xyz[2], max_xyz[2], buffer_factor=1.25)
return xlim, ylim, zlim
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_samples_plots(self, sample_lists, m, color, label):
"""
Update the samples plots with iteration data, for the trajectory samples
and the policy samples.
"""
samples = sample_lists[m].get_samples()
for sample in samples:
ee_pt = sample.get(END_EFFECTOR_POINTS)
for i in range(ee_pt.shape[1] / 3):
ee_pt_i = ee_pt[:, 3 * i + 0:3 * i + 3]
self._traj_visualizer.plot_3d_points(m,
ee_pt_i,
color=color,
label=label)
def save_figure(self, filename):
self._fig.savefig(filename)
class TransferLearningGUI(object):
def __init__(self, hyperparams, agent):
self._agent = agent
self._hyperparams = hyperparams
self._log_filename = self._hyperparams['log_filename']
if 'target_filename' in self._hyperparams:
self._target_filename = self._hyperparams['target_filename']
else:
self._target_filename = None
# GPS Training Status.
self.mode = config['initial_mode'] # Modes: run, wait, end, request, process.
self.request = None # Requests: stop, reset, go, fail, None.
self.err_msg = None
self._colors = {
'run': 'cyan',
'wait': 'orange',
'end': 'red',
}
self._actuator_types = config['actuator_types']
self._actuator_names = config['actuator_names']
self._first_update = True
self._actuator_number = 0
self._actuator_type = self._actuator_types[self._actuator_number]
self._initial_position = ('unknown', 'unknown', 'unknown')
self._target_position = ('unknown', 'unknown', 'unknown')
# Actions.
actions_arr = [
Action('stop', 'stop', self.request_stop, axis_pos=0),
Action('reset', 'reset', self.request_reset, axis_pos=1),
Action('GCM go', 'go', self.request_go, axis_pos=2),
Action('transfer learning', 'transfer_learning', self.request_tl, axis_pos=3),
Action('set initial state', 'initstate', self.request_init_state, axis_pos=4),
Action('set goal state', 'goalstate', self.request_goal_state, axis_pos=5),
Action('test transfer learning', 'test_tl', self.request_test_tl, axis_pos=6),
Action('generalize', 'generalize', self.request_generalize, axis_pos=7),
Action('mti', 'move_to_initial', self.move_to_initial, axis_pos=8),
Action('mtt', 'move_to_target', self.move_to_target, axis_pos=9),
Action('rc', 'relax_controller', self.relax_controller, axis_pos=10),
]
# Setup figure.
plt.ion()
plt.rcParams['toolbar'] = 'None'
for key in plt.rcParams:
if key.startswith('keymap.'):
plt.rcParams[key] = ''
self._fig = plt.figure(figsize=config['figsize'])
self._fig.subplots_adjust(left=0.01, bottom=0.01, right=0.99, top=0.99,
wspace=0, hspace=0)
# Assign GUI component locations.
self._gs = gridspec.GridSpec(18, 8)
self._gs_action_panel = self._gs[0:4, 0:8]
self._gs_action_output = self._gs[4:5, 0:4]
self._gs_status_output = self._gs[5:6, 0:4]
self._gs_cost_plotter = self._gs[4:10, 4:8]
self._gs_algthm_output = self._gs[6:10, 0:4]
self._gs_traj_visualizer = self._gs[10:18, 0:8]
# Create GUI components.
self._action_panel = ActionPanel(self._fig, self._gs_action_panel, 3, 4, actions_arr)
self._action_output = Textbox(self._fig, self._gs_action_output, border_on=True)
self._status_output = Textbox(self._fig, self._gs_status_output, border_on=False)
self._algthm_output = Textbox(self._fig, self._gs_algthm_output,
max_display_size=config['algthm_output_max_display_size'],
log_filename=self._log_filename,
fontsize=config['algthm_output_fontsize'],
font_family='monospace')
self._cost_plotter = MeanPlotter(self._fig, self._gs_cost_plotter,
color='blue', label='mean cost')
self._traj_visualizer = Plotter3D(self._fig, self._gs_traj_visualizer,
num_plots=self._hyperparams['conditions'])
# Setup GUI components.
self._algthm_output.log_text('\n')
self.set_output_text(self._hyperparams['info'])
if config['initial_mode'] == 'run':
self.run_mode()
else:
self.wait_mode()
# Setup 3D Trajectory Visualizer plot titles and legends
for m in range(self._hyperparams['conditions']):
self._traj_visualizer.set_title(m, 'Condition %d' % (m))
self._traj_visualizer.add_legend(linestyle='-', marker='None',
color='green', label='Trajectory Samples')
self._traj_visualizer.add_legend(linestyle='-', marker='None',
color='blue', label='Policy Samples')
self._traj_visualizer.add_legend(linestyle='None', marker='x',
color=(0.5, 0, 0), label='LG Controller Means')
self._traj_visualizer.add_legend(linestyle='-', marker='None',
color='red', label='LG Controller Distributions')
self._fig.canvas.draw()
# Display calculating thread
def display_calculating(delay, run_event):
while True:
if not run_event.is_set():
run_event.wait()
if run_event.is_set():
self.set_status_text('Calculating.')
time.sleep(delay)
if run_event.is_set():
self.set_status_text('Calculating..')
time.sleep(delay)
if run_event.is_set():
self.set_status_text('Calculating...')
time.sleep(delay)
self._calculating_run = threading.Event()
self._calculating_thread = threading.Thread(target=display_calculating,
args=(1, self._calculating_run))
self._calculating_thread.daemon = True
self._calculating_thread.start()
# GPS Training functions
def request_stop(self, event=None):
self.request_mode('stop')
self._agent.sample(
self._agent.policy,
self._agent.condition, verbose=None,
save=False, noisy=False,
use_TfController=True, timeout=0, reset=False)
def request_reset(self, event=None):
self.request_mode('reset')
self._agent.reset(self._agent.condition)
def request_go(self, event=None):
self.request_mode('go')
def request_tl(self, event=None):
self.request_mode('transfer_learning')
def request_test_tl(self, event=None):
self.request_mode('test_tl')
def request_generalize(self, event=None):
self.request_mode('generalize')
def move_to_initial(self, event=None):
ja = self._initial_position[0]
self.set_action_status_message('move_to_initial', 'requested')
self._agent.reset_arm(self._actuator_type, JOINT_SPACE, ja.T)
self.set_action_status_message('move_to_initial', 'completed',
message='initial position: %s' % str(ja))
def move_to_target(self, event=None):
ja = self._target_position[0]
self.set_action_status_message('move_to_target', 'requested')
self._agent.reset_arm(self._actuator_type, JOINT_SPACE, ja.T)
self.set_action_status_message('move_to_target', 'completed',
message='target position: %s' % str(ja))
def relax_controller(self, event=None):
self.set_action_status_message('relax_controller', 'requested')
self._agent.relax_arm(self._actuator_type)
self.set_action_status_message('relax_controller', 'completed',
message='actuator name: %s' % self._actuator_name)
def request_goal_state(self, event=None):
self.request_mode('goalstate')
sample = self._agent.get_data(arm=self._actuator_type)
ja = sample.get(JOINT_ANGLES)
ee_pos = sample.get(END_EFFECTOR_POSITIONS)
ee_rot = sample.get(END_EFFECTOR_ROTATIONS)
ee_tgt = np.ndarray.flatten(
get_ee_points(self._agent._hyperparams['ee_points'], ee_pos, ee_rot).T
)
self._agent._hyperparams['ee_points_tgt'] = [ee_tgt]
self._target_position = (ja, ee_pos, ee_rot)
self._agent._target_ja = [ja]
def request_init_state(self, event=None):
self.request_mode('initstate')
sample = self._agent.get_data(arm=self._actuator_type)
ja = sample.get(JOINT_ANGLES)
ee_pos = sample.get(END_EFFECTOR_POSITIONS)
ee_rot = sample.get(END_EFFECTOR_ROTATIONS)
self._initial_position = (ja, ee_pos, ee_rot)
reset_condition = {
TRIAL_ARM: {
'mode': JOINT_SPACE,
'data': ja[0],
},
}
res_cons = []
res_cons.append(reset_condition)
self._agent._initial_ja = [ja]
def request_mode(self, request):
"""
Sets the request mode (stop, reset, go, fail). The request is read by
gps_main before sampling, and the appropriate action is taken.
"""
self.mode = 'request'
self.request = request
self.set_action_text(self.request + ' requested')
#self.set_action_bgcolor(self._colors[self.request], alpha=0.2)
def process_mode(self):
"""
Completes the current request, after it is first read by gps_main.
Displays visual confirmation that the request was processed,
displays any error messages, and then switches into mode 'run' or 'wait'.
"""
self.mode = 'process'
self.set_action_text(self.request + ' processed')
self.set_action_bgcolor(self._colors[self.request], alpha=1.0)
if self.err_msg:
self.set_action_text(self.request + ' processed' + '\nERROR: ' +
self.err_msg)
self.err_msg = None
time.sleep(1.0)
else:
time.sleep(0.5)
if self.request in ('stop', 'reset', 'fail'):
self.wait_mode()
elif self.request == 'go':
self.run_mode()
self.request = None
def wait_mode(self):
self.mode = 'wait'
self.set_action_text('waiting')
self.set_action_bgcolor(self._colors[self.mode], alpha=1.0)
def run_mode(self):
self.mode = 'run'
self.set_action_text('running')
self.set_action_bgcolor(self._colors[self.mode], alpha=1.0)
def end_mode(self):
self.mode = 'end'
self.set_action_text('ended')
self.set_action_bgcolor(self._colors[self.mode], alpha=1.0)
def estop(self, event=None):
self.set_action_text('estop: NOT IMPLEMENTED')
# GUI functions
def set_action_text(self, text):
self._action_output.set_text(text)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
def set_action_bgcolor(self, color, alpha=1.0):
self._action_output.set_bgcolor(color, alpha)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
def set_status_text(self, text):
self._status_output.set_text(text)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
def set_output_text(self, text):
self._algthm_output.set_text(text)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
def append_output_text(self, text):
self._algthm_output.append_text(text)
self._cost_plotter.draw_ticklabels() # redraw overflow ticklabels
def start_display_calculating(self):
self._calculating_run.set()
def stop_display_calculating(self):
self._calculating_run.clear()
def set_action_status_message(self, action, status, message=None):
text = action + ': ' + status
if message:
text += '\n\n' + message
self.set_action_text(text)
if status == 'requested':
self.set_action_bgcolor('yellow')
elif status == 'completed':
self.set_action_bgcolor('green')
elif status == 'failed':
self.set_action_bgcolor('red')
def set_image_overlays(self, condition):
"""
Sets up the image visualizer with what images to overlay if
"overlay_initial_image" or "overlay_target_image" is pressed.
"""
if not config['image_on'] or not self._target_filename:
return
initial_image = load_data_from_npz(self._target_filename,
config['image_overlay_actuator'], str(condition),
'initial', 'image', default=None)
target_image = load_data_from_npz(self._target_filename,
config['image_overlay_actuator'], str(condition),
'target', 'image', default=None)
self._image_visualizer.set_initial_image(initial_image,
alpha=config['image_overlay_alpha'])
self._image_visualizer.set_target_image(target_image,
alpha=config['image_overlay_alpha'])
# Iteration update functions
def update(self, itr, algorithm, agent, traj_sample_lists, pol_sample_lists):
"""
After each iteration, update the iteration data output, the cost plot,
and the 3D trajectory visualizations (if end effector points exist).
"""
if self._first_update:
self._output_column_titles(algorithm)
self._first_update = False
costs = [np.mean(np.sum(algorithm.prev[m].cs, axis=1)) for m in range(algorithm.M)]
self._update_iteration_data(itr, algorithm, costs, pol_sample_lists)
self._cost_plotter.update(costs, t=itr)
if END_EFFECTOR_POINTS in agent.x_data_types:
self._update_trajectory_visualizations(algorithm, agent,
traj_sample_lists, pol_sample_lists)
self._fig.canvas.draw()
self._fig.canvas.flush_events() # Fixes bug in Qt4Agg backend
def _output_column_titles(self, algorithm, policy_titles=False):
"""
Setup iteration data column titles: iteration, average cost, and for
each condition the mean cost over samples, step size, linear Guassian
controller entropies, and initial/final KL divergences for BADMM.
"""
self.set_output_text(self._hyperparams['experiment_name'])
condition_titles = '%3s | %8s' % ('', '')
itr_data_fields = '%3s | %8s' % ('itr', 'avg_cost')
for m in range(algorithm.M):
condition_titles += ' | %8s %9s %-7d' % ('', 'condition', m)
itr_data_fields += ' | %8s %8s %8s' % (' cost ', ' step ', 'entropy ')
self.append_output_text(condition_titles)
self.append_output_text(itr_data_fields)
def _update_iteration_data(self, itr, algorithm, costs, pol_sample_lists):
"""
Update iteration data information: iteration, average cost, and for
each condition the mean cost over samples, step size, linear Guassian
controller entropies, and initial/final KL divergences for BADMM.
"""
avg_cost = np.mean(costs)
if pol_sample_lists is not None:
test_idx = algorithm._hyperparams['test_conditions']
print("tet_idx: ", test_idx)
# pol_sample_lists is a list of singletons
samples = [sl[0] for sl in pol_sample_lists]
print("samples len: ", len(samples))
print("algorithm.costs: ", len(algorithm.cost))
print("costs: ", algorithm.cost[-1].eval(samples[0]))
#print("algorithm.cost.eval: ", len(algorithm.cost[-1].eval))
pol_costs = [np.sum(algorithm.cost[idx].eval(s)[0])
for s, idx in zip(samples, test_idx)]
itr_data = '%3d | %8.2f %12.2f' % (itr, avg_cost, np.mean(pol_costs))
else:
itr_data = '%3d | %8.2f' % (itr, avg_cost)
for m in range(algorithm.M):
cost = costs[m]
step = algorithm.prev[m].step_mult * algorithm.base_kl_step
entropy = 2*np.sum(np.log(np.diagonal(algorithm.prev[m].traj_distr.chol_pol_covar,
axis1=1, axis2=2)))
itr_data += ' | %8.2f %8.2f %8.2f' % (cost, step, entropy)
self.append_output_text(itr_data)
def _update_trajectory_visualizations(self, algorithm, agent,
traj_sample_lists, pol_sample_lists):
"""
Update 3D trajectory visualizations information: the trajectory samples,
policy samples, and linear Gaussian controller means and covariances.
"""
xlim, ylim, zlim = self._calculate_3d_axis_limits(traj_sample_lists, pol_sample_lists)
for m in range(algorithm.M):
self._traj_visualizer.clear(m)
self._traj_visualizer.set_lim(i=m, xlim=xlim, ylim=ylim, zlim=zlim)
self._update_linear_gaussian_controller_plots(algorithm, agent, m)
self._update_samples_plots(traj_sample_lists, m, 'green', 'Trajectory Samples')
if pol_sample_lists:
self._update_samples_plots(pol_sample_lists, m, 'blue', 'Policy Samples')
self._traj_visualizer.draw() # this must be called explicitly
def _calculate_3d_axis_limits(self, traj_sample_lists, pol_sample_lists):
"""
Calculate the 3D axis limits shared between trajectory plots,
based on the minimum and maximum xyz values across all samples.
"""
all_eept = np.empty((0, 3))
sample_lists = traj_sample_lists
if pol_sample_lists:
sample_lists += traj_sample_lists
for sample_list in sample_lists:
for sample in sample_list.get_samples():
ee_pt = sample.get(END_EFFECTOR_POINTS)
for i in range(ee_pt.shape[1]/3):
ee_pt_i = ee_pt[:, 3*i+0:3*i+3]
all_eept = np.r_[all_eept, ee_pt_i]
min_xyz = np.amin(all_eept, axis=0)
max_xyz = np.amax(all_eept, axis=0)
xlim = buffered_axis_limits(min_xyz[0], max_xyz[0], buffer_factor=1.25)
ylim = buffered_axis_limits(min_xyz[1], max_xyz[1], buffer_factor=1.25)
zlim = buffered_axis_limits(min_xyz[2], max_xyz[2], buffer_factor=1.25)
return xlim, ylim, zlim
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.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_samples_plots(self, sample_lists, m, color, label):
"""
Update the samples plots with iteration data, for the trajectory samples
and the policy samples.
"""
samples = sample_lists[m].get_samples()
for sample in samples:
ee_pt = sample.get(END_EFFECTOR_POINTS)
for i in range(ee_pt.shape[1]/3):
ee_pt_i = ee_pt[:, 3*i+0:3*i+3]
self._traj_visualizer.plot_3d_points(m, ee_pt_i, color=color, label=label)
def save_figure(self, filename):
self._fig.savefig(filename)