def create_single_parkour_env(env_name,
seed,
use_monitor,
split='',
mujoco_key_path=None,
run_oracle_before_monitor=False):
"""Creates a parkour environment."""
del env_name # unused
del split # unused
print('Creating parkour env')
env = ant_wrapper.AntWrapper(height=Const.OBSERVATION_HEIGHT,
width=Const.OBSERVATION_WIDTH,
mujoco_key_path=mujoco_key_path)
if run_oracle_before_monitor:
env = dmlab_utils.OracleRewardWrapper(env)
if use_monitor:
env = Monitor(
env,
logger.get_dir() and os.path.join(logger.get_dir(), str(seed)))
return env
def create_single_env(env_name, seed, dmlab_homepath, use_monitor,
split='train', vizdoom_maze=False, action_set='',
respawn=True, fixed_maze=False, maze_size=None,
room_count=None, episode_length_seconds=None,
min_goal_distance=None, run_oracle_before_monitor=False):
"""Creates a single instance of DMLab env, with training mixer seed.
Args:
env_name: Name of the DMLab environment.
seed: seed passed to DMLab. Must be != 0.
dmlab_homepath: Path to DMLab MPM. Required when running on borg.
use_monitor: Boolean to add a Monitor wrapper.
split: One of {"train", "valid", "test"}.
vizdoom_maze: Whether a geometry of a maze should correspond to the one used
by Pathak in his curiosity paper in Vizdoom environment.
action_set: One of {'small', 'nofire', ''}. Which action set to use.
respawn: If disabled respawns are not allowed
fixed_maze: Boolean to use predefined maze configuration.
maze_size: If not None sets particular height/width for mazes to be
generated.
room_count: If not None sets the number of rooms for mazes to be generated.
episode_length_seconds: If not None overrides the episode duration.
min_goal_distance: If not None ensures that there's at least this distance
between the starting and target location (for
explore_goal_locations_large level).
run_oracle_before_monitor: Whether to run OracleRewardWrapper before the
Monitor.
Returns:
Gym compatible DMLab env.
Raises:
ValueError: when the split is invalid.
"""
main_observation = 'DEBUG.CAMERA.PLAYER_VIEW_NO_RETICLE'
level = constants.Const.find_level(env_name)
env_settings = dmlab_utils.create_env_settings(
level.dmlab_level_name,
homepath=dmlab_homepath,
width=Const.OBSERVATION_WIDTH,
height=Const.OBSERVATION_HEIGHT,
seed=seed,
main_observation=main_observation)
env_settings.update(level.extra_env_settings)
if maze_size:
env_settings['mazeHeight'] = maze_size
env_settings['mazeWidth'] = maze_size
if min_goal_distance:
env_settings['minGoalDistance'] = min_goal_distance
if room_count:
env_settings['roomCount'] = room_count
if episode_length_seconds:
env_settings['episodeLengthSeconds'] = episode_length_seconds
if split == 'train':
mixer_seed = Const.MIXER_SEEDS[constants.SplitType.POLICY_TRAINING]
elif split == 'valid':
mixer_seed = Const.MIXER_SEEDS[constants.SplitType.VALIDATION]
elif split == 'test':
mixer_seed = Const.MIXER_SEEDS[constants.SplitType.TEST]
else:
raise ValueError('Invalid split: {}'.format(split))
env_settings.update(mixerSeed=mixer_seed)
if vizdoom_maze:
env_settings['episodeLengthSeconds'] = 60
env_settings['overrideEntityLayer'] = """*******************
***** * *** *
***** *
***** * *** *
****** *** ***** **
* * * *** *
*P *** *
* * * *** *
****** ********* **
****** *********G**
***** ***********
***** ***********
***** ***********
****** ************
****** ************
****** **********
*******************"""
if fixed_maze:
env_settings['overrideEntityLayer'] = """
*****************
* *PPG *
* *** * *PPP*** *
* *GPP* *GGG PGP*
* *GPG* * ***PGP*
* *PGP* ***PGG*
* *********** * *
* *GPG*GGP *
* *** *PPG*PGG* *
*PGP* *GPP PPP* *
*PPP* * *** *** *
*GGG* *GPP* *
*** ***** *GGG* *
*GPG PPG PPP* *
*PGP*GGP* ***** *
*GPP*GPP* *
*****************"""
# Gym compatible environment.
env = dmlab_utils.DMLabWrapper(
'dmlab',
env_settings,
action_set=get_action_set(action_set),
main_observation=main_observation)
env = atari_wrappers.StickyActionEnv(env)
env = CollectGymDataset(env, os.path.expanduser(FLAGS.ep_path), atari=False)
if run_oracle_before_monitor:
env = dmlab_utils.OracleRewardWrapper(env)
if vizdoom_maze or not respawn:
env = dmlab_utils.EndEpisodeOnRespawn(env)
if use_monitor:
env = Monitor(
env, logger.get_dir() and os.path.join(logger.get_dir(), str(seed)))
return env