def make_robotics_env(env_id, seed, rank=0, allow_early_resets=True):
"""
Create a wrapped, monitored gym.Env for MuJoCo.
:param env_id: (str) the environment ID
:param seed: (int) the inital seed for RNG
:param rank: (int) the rank of the environment (for logging)
:param allow_early_resets: (bool) allows early reset of the environment
:return: (Gym Environment) The robotic environment
"""
set_global_seeds(seed)
env = gym.make(env_id)
keys = ['observation', 'desired_goal']
# TODO: remove try-except once most users are running modern Gym
try: # for modern Gym (>=0.15.4)
from gym.wrappers import FilterObservation, FlattenObservation
env = FlattenObservation(FilterObservation(env, keys))
except ImportError: # for older gym (<=0.15.3)
from gym.wrappers import FlattenDictWrapper # pytype:disable=import-error
env = FlattenDictWrapper(env, keys)
env = Monitor(env,
logger.get_dir()
and os.path.join(logger.get_dir(), str(rank)),
info_keywords=('is_success', ),
allow_early_resets=allow_early_resets)
env.seed(seed)
return env
def make_env(env_id, env_type, mpi_rank=0, subrank=0, seed=None, reward_scale=1.0, gamestate=None, flatten_dict_observations=True, wrapper_kwargs=None, logger_dir=None):
wrapper_kwargs = wrapper_kwargs or {}
if env_type == 'atari':
env = make_atari(env_id)
elif env_type == 'retro':
import retro
gamestate = gamestate or retro.State.DEFAULT
env = retro_wrappers.make_retro(game=env_id, max_episode_steps=10000, use_restricted_actions=retro.Actions.DISCRETE, state=gamestate)
else:
env = gym.make(env_id)
if flatten_dict_observations and isinstance(env.observation_space, gym.spaces.Dict):
keys = env.observation_space.spaces.keys()
env = FlattenObservation(env, dict_keys=list(keys))
env.seed(seed + subrank if seed is not None else None)
env = Monitor(env,
logger_dir and os.path.join(logger_dir, str(mpi_rank) + '.' + str(subrank)),
allow_early_resets=True)
if env_type == 'atari':
env = wrap_deepmind(env, **wrapper_kwargs)
elif env_type == 'retro':
if 'frame_stack' not in wrapper_kwargs:
wrapper_kwargs['frame_stack'] = 1
env = retro_wrappers.wrap_deepmind_retro(env, **wrapper_kwargs)
if reward_scale != 1:
env = retro_wrappers.RewardScaler(env, reward_scale)
return env
def make_env(env_id, env_type, mpi_rank=0, subrank=0, seed=None, reward_scale=1.0, gamestate=None,
flatten_dict_observations=True, wrapper_kwargs=None, env_kwargs=None, logger_dir=None, initializer=None):
if initializer is not None:
initializer(mpi_rank=mpi_rank, subrank=subrank)
wrapper_kwargs = wrapper_kwargs or {}
env_kwargs = env_kwargs or {}
if ':' in env_id:
import importlib
import re
module_name = re.sub(':.*','',env_id)
env_id = re.sub('.*:', '', env_id)
importlib.import_module(module_name)
env = gym.make(env_id, **env_kwargs)
# if env_id.startswith('Sawyer'):
# from mher.algos.multi_world_wrapper import SawyerGoalWrapper
# env = SawyerGoalWrapper(env)
# if (env_id.startswith('Sawyer') or env_id.startswith('Point2D')) and not hasattr(env, '_max_episode_steps'):
# env = gym.wrappers.TimeLimit(env, max_episode_steps=100)
if flatten_dict_observations and isinstance(env.observation_space, gym.spaces.Dict):
env = FlattenObservation(env)
env.seed(seed + subrank if seed is not None else None)
env = Monitor(env,
logger_dir and os.path.join(logger_dir, str(mpi_rank) + '.' + str(subrank)),
allow_early_resets=True)
if isinstance(env.action_space, gym.spaces.Box):
env = ClipActionsWrapper(env)
if reward_scale != 1:
env = retro_wrappers.RewardScaler(env, reward_scale)
return env
def _thunk():
env = gym.make(env_id)
if env_id.find('Fetch') == -1:
env = FlattenObservation(env)
else:
env = FlattenDictWrapper(env, ['achieved_goal', 'desired_goal'])
env = RandomizedEnvWrapper(env, seed + rank)
env.seed(seed + rank)
return env
def make_robotics_env(env_id, seed, rank=0):
"""
Create a wrapped, monitored gym.Env for MuJoCo.
"""
set_global_seeds(seed)
env = gym.make(env_id)
env = FlattenObservation(FilterObservation(env, ['observation', 'desired_goal']))
env = Monitor(
env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)),
info_keywords=('is_success',))
env.seed(seed)
return env
def make_env(env_id, env_type, mpi_rank=0, subrank=0, seed=None, reward_scale=1.0, gamestate=None, flatten_dict_observations=True, wrapper_kwargs=None, env_kwargs=None, logger_dir=None, initializer=None):
if initializer is not None:
initializer(mpi_rank=mpi_rank, subrank=subrank)
wrapper_kwargs = wrapper_kwargs or {}
env_kwargs = env_kwargs or {}
if ':' in env_id:
import re
import importlib
module_name = re.sub(':.*','',env_id)
env_id = re.sub('.*:', '', env_id)
importlib.import_module(module_name)
if env_type == 'atari':
env = make_atari(env_id)
elif env_type == 'retro':
import retro
gamestate = gamestate or retro.State.DEFAULT
env = retro_wrappers.make_retro(game=env_id, max_episode_steps=10000, use_restricted_actions=retro.Actions.DISCRETE, state=gamestate)
else:
env = gym.make(env_id, **env_kwargs)
if flatten_dict_observations and isinstance(env.observation_space, gym.spaces.Dict):
env = FlattenObservation(env)
env.seed(seed + subrank if seed is not None else None)
env = Monitor(env,
logger_dir and os.path.join(logger_dir, str(mpi_rank) + '.' + str(subrank)),
allow_early_resets=True)
if env_type == 'atari':
env = wrap_deepmind(env, **wrapper_kwargs)
elif env_type == 'retro':
if 'frame_stack' not in wrapper_kwargs:
wrapper_kwargs['frame_stack'] = 1
env = retro_wrappers.wrap_deepmind_retro(env, **wrapper_kwargs)
if isinstance(env.action_space, gym.spaces.Box):
env = ClipActionsWrapper(env)
if reward_scale != 1:
env = retro_wrappers.RewardScaler(env, reward_scale)
return env
def make_robotics_env(env_id, seed, rank=0):
"""
Create a wrapped, monitored gym.Env for MuJoCo.
"""
set_global_seeds(seed)
env = gym.make(env_id)
#env = FlattenDictWrapper(env, ['observation', 'desired_goal'])
keys = ['observation', 'desired_goal']
# TODO: remove try-except once most users are running modern Gym
try: # for modern Gym (>=0.15.4)
from gym.wrappers import FilterObservation, FlattenObservation
env = FlattenObservation(FilterObservation(env, keys))
except ImportError: # for older gym (<=0.15.3)
from gym.wrappers import FlattenDictWrapper # pytype:disable=import-error
env = FlattenDictWrapper(env, keys)
env = Monitor(env,
logger.get_dir()
and os.path.join(logger.get_dir(), str(rank)),
info_keywords=('is_success', ))
env.seed(seed)
return env
def make_env(env_id,
env_type,
mpi_rank=0,
subrank=0,
seed=None,
reward_scale=1.0,
gamestate=None,
flatten_dict_observations=True,
wrapper_kwargs=None,
env_kwargs=None,
logger_dir=None,
initializer=None):
"""
Make environment
Args:
env_id: (str) environment id e.g. 'Reacher-v2'
env_type: (str) environment type e.g. 'atari'
mpi_rank: (int) rank for mpi; default=0 (disabled on windows for lack of MPI support from pytorch)
subrank: (int) subrank; default=0 (disabled on windows for lack of MPI support from pytorch)
seed: (int) random seed
reward_scale: (float) scale factor for reward (== discount factor??); default=1.0
gamestate: (??) game state to load (for retro games only)
flatten_dict_observations: (??) ??
wrapper_kwargs: (dict) dictionary of parameter settings for wrapper
env_kwargs: (dict) dictionary of parameter settings for environment
logger_dir: (str) logger path
initializer: (??) ??
Returns:
env: (Env) the set-up environment
"""
if initializer is not None:
initializer(mpi_rank=mpi_rank, subrank=subrank)
wrapper_kwargs = wrapper_kwargs or {}
env_kwargs = env_kwargs or {}
if ':' in env_id:
raise ValueError(
"env_id {} does not conform to accepted format!".format(env_id))
if env_type == 'atari':
# make atari environments with a wrapper function
env = make_atari(env_id)
elif env_type == 'retro':
raise ValueError("retro environments not supported yet!")
else:
# make a gym environment with parameter settings
env = gym.make(env_id, **env_kwargs)
# flatten the observation space
if flatten_dict_observations and isinstance(env.observation_spaces,
gym.spaces.Dict):
env = FlattenObservation(env)
# add seed to env
env.seed(seed + subrank if seed is not None else None)
# set up Monitor (TBD)
if env_type == 'atari':
env = wrap_deepmind(env, **wrapper_kwargs)
elif env_type == 'retro':
if 'frame_stack' not in wrapper_kwargs:
wrapper_kwargs['frame_stack'] = 1
# wrap retro games
env = wrappers_retro.wrap_deepmind_retro(env, **wrapper_kwargs)
if isinstance(env.action_space, gym.spaces.Box):
# if action_space is Box type, clip the action values to be within the box's boundaries
env = wrappers.ClipActionsWrapper(env)
if reward_scale != 1:
# if reward scaling factor is used, scale the rewards accordingly
# very important feature for PPO
env = wrappers.RewardScalerWrapper(env, reward_scale)
return env
