def log_diagnostics(self, paths, logger=default_logger):
lms = get_stat_in_paths(paths, 'agent_infos', 'lagrange_multiplier')
for key, value in create_stats_ordered_dict(
"TDM LBFGS Lagrange Multiplier",
lms,
).items():
logger.record_tabular(key, value)
def log_diagnostics(self, paths, logger=default_logger):
statistics = OrderedDict()
for name_in_env_infos, name_to_log in [
('distance_to_target', 'Distance to Target'),
('speed', 'Speed'),
('distance_reward', 'Distance Reward'),
('action_reward', 'Action Reward'),
]:
stats = get_stat_in_paths(paths, 'env_infos', name_in_env_infos)
statistics.update(create_stats_ordered_dict(
name_to_log,
stats,
))
final_stats = [s[-1] for s in stats]
statistics.update(
create_stats_ordered_dict(
"Final " + name_to_log,
final_stats,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
"Path Lengths",
get_path_lengths(paths),
))
for key, value in statistics.items():
logger.record_tabular(key, value)
def log_diagnostics(self, paths, logger=default_logger):
statistics = OrderedDict()
for name_in_env_infos, name_to_log in [
('distance_to_target', 'Distance to Target'),
('reward_ctrl', 'Action Reward'),
]:
stat = get_stat_in_paths(paths, 'env_infos', name_in_env_infos)
statistics.update(create_stats_ordered_dict(
name_to_log,
stat,
))
distances = get_stat_in_paths(paths, 'env_infos', 'distance_to_target')
statistics.update(create_stats_ordered_dict(
"Final Distance to Target",
[ds[-1] for ds in distances],
))
for key, value in statistics.items():
logger.record_tabular(key, value)
def log_diagnostics(self, paths):
reward_fwd = get_stat_in_paths(paths, 'env_infos', 'reward_fwd')
reward_ctrl = get_stat_in_paths(paths, 'env_infos', 'reward_ctrl')
logger.record_tabular('AvgRewardDist', np.mean(reward_fwd))
logger.record_tabular('AvgRewardCtrl', np.mean(reward_ctrl))
if len(paths) > 0:
progs = [
path["observations"][-1][-3] - path["observations"][0][-3]
for path in paths
]
logger.record_tabular('AverageForwardProgress', np.mean(progs))
logger.record_tabular('MaxForwardProgress', np.max(progs))
logger.record_tabular('MinForwardProgress', np.min(progs))
logger.record_tabular('StdForwardProgress', np.std(progs))
else:
logger.record_tabular('AverageForwardProgress', np.nan)
logger.record_tabular('MaxForwardProgress', np.nan)
logger.record_tabular('MinForwardProgress', np.nan)
logger.record_tabular('StdForwardProgress', np.nan)
def log_diagnostics(self, paths):
statistics = OrderedDict()
for stat_name in [
'arm to object distance',
'object to goal distance',
'arm to goal distance',
]:
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(create_stats_ordered_dict(stat_name, stat))
for key, value in statistics.items():
logger.record_tabular(key, value)
def log_diagnostics(self, paths, logger=default_logger):
statistics = OrderedDict()
for name_in_env_infos, name_to_log in [
('posafter', 'Position'),
('height', 'Height'),
('angle', 'Angle'),
]:
stats = get_stat_in_paths(paths, 'env_infos', name_in_env_infos)
statistics.update(create_stats_ordered_dict(
name_to_log,
stats,
))
statistics.update(
create_stats_ordered_dict(
"Final " + name_to_log,
[s[-1] for s in stats],
))
for key, value in statistics.items():
logger.record_tabular(key, value)
def get_diagnostics(self, paths):
statistics = OrderedDict()
for stat_name_in_paths, stat_name_to_print in [
('arm_object_distance', 'Distance hand to object'),
('arm_goal_distance', 'Distance hand to goal'),
]:
stats = get_stat_in_paths(paths, 'env_infos', stat_name_in_paths)
statistics.update(create_stats_ordered_dict(
stat_name_to_print,
stats,
always_show_all_stats=True,
))
final_stats = [s[-1] for s in stats]
statistics.update(create_stats_ordered_dict(
"Final " + stat_name_to_print,
final_stats,
always_show_all_stats=True,
))
return statistics
def log_diagnostics(self, paths, logger=default_logger):
statistics = OrderedDict()
for stat_name_in_paths, stat_name_to_print in [
('hand_to_object_distance', 'Distance hand to object'),
('object_to_goal_distance', 'Distance object to goal'),
('hand_to_hand_goal_distance', 'Distance hand to hand goal'),
('success', 'Success (within 0.1)'),
]:
stats = get_stat_in_paths(paths, 'env_infos', stat_name_in_paths)
statistics.update(
create_stats_ordered_dict(
stat_name_to_print,
stats,
always_show_all_stats=True,
))
final_stats = [s[-1] for s in stats]
statistics.update(
create_stats_ordered_dict(
"Final " + stat_name_to_print,
final_stats,
always_show_all_stats=True,
))
for key, value in statistics.items():
logger.record_tabular(key, value)
