def log_diagnostics(self, paths, *args, **kwargs):
# we call here any logging related to the maze, strip the maze
# obs and call log_diag with the stripped paths we need to log
# the purely gather reward!!
with logger.tabular_prefix('Maze_'):
gather_undiscounted_returns = [
sum(path['env_infos']['outer_rew']) for path in paths
]
logger.record_tabular_misc_stat('Return',
gather_undiscounted_returns,
placement='front')
stripped_paths = []
for path in paths:
stripped_path = {}
for k, v in path.items():
stripped_path[k] = v
stripped_path['observations'] = stripped_path[
'observations'][:, :flat_dim(self.env.observation_space)]
# this breaks if the obs of the robot are d>1 dimensional (not a
# vector)
stripped_paths.append(stripped_path)
with logger.tabular_prefix('wrapped_'):
wrapped_undiscounted_return = np.mean(
[np.sum(path['env_infos']['inner_rew']) for path in paths])
logger.record_tabular('AverageReturn', wrapped_undiscounted_return)
self.env.log_diagnostics(stripped_paths, *args, **kwargs)
def log_diagnostics(self, paths):
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))