def __init__(self,
domain,
task,
*args,
normalize=True,
observation_keys=None,
unwrap_time_limit=True,
**kwargs):
self.normalize = normalize
self.observation_keys = observation_keys
self.unwrap_time_limit = unwrap_time_limit
self._Serializable__initialize(locals())
super(GymAdapter, self).__init__(domain, task, *args, **kwargs)
env = GYM_ENVIRONMENTS[domain][task](*args, **kwargs)
if isinstance(env, wrappers.TimeLimit) and unwrap_time_limit:
# Remove the TimeLimit wrapper that sets 'done = True' when
# the time limit specified for each environment has been passed and
# therefore the environment is not Markovian (terminal condition
# depends on time rather than state).
env = env.env
if isinstance(env.observation_space, spaces.Dict):
observation_keys = (observation_keys
or list(env.observation_space.spaces.keys()))
if normalize:
env = NormalizeActionWrapper(env)
self._env = env
def __init__(self,
domain,
task,
*args,
env=None,
normalize=True,
observation_keys=None,
unwrap_time_limit=True,
**kwargs):
assert not args, (
"Gym environments don't support args. Use kwargs instead.")
self.normalize = normalize
self.observation_keys = observation_keys
self.unwrap_time_limit = unwrap_time_limit
super(GymAdapter, self).__init__(domain, task, *args, **kwargs)
if env is None:
assert (domain is not None and task is not None), (domain, task)
env_id = "{}-{}".format(domain, task)
env = gym.envs.make(env_id, **kwargs)
else:
assert domain is None and task is None, (domain, task)
if isinstance(env, wrappers.TimeLimit) and unwrap_time_limit:
# Remove the TimeLimit wrapper that sets 'done = True' when
# the time limit specified for each environment has been passed and
# therefore the environment is not Markovian (terminal condition
# depends on time rather than state).
env = env.env
if normalize:
env = NormalizeActionWrapper(env)
self._env = env
if isinstance(self._env.observation_space, spaces.Dict):
dict_observation_space = self._env.observation_space
self.observation_keys = (
observation_keys
or (*self._env.observation_space.spaces.keys(), ))
elif isinstance(self._env.observation_space, spaces.Box):
dict_observation_space = spaces.Dict(
OrderedDict(((DEFAULT_OBSERVATION_KEY,
self._env.observation_space), )))
self.observation_keys = (DEFAULT_OBSERVATION_KEY, )
self._observation_space = type(dict_observation_space)([
(name, copy.deepcopy(space))
for name, space in dict_observation_space.spaces.items()
if name in self.observation_keys
])
def __init__(self,
domain,
task,
*args,
env=None,
normalize=True,
observation_keys=None,
unwrap_time_limit=True,
**kwargs):
assert not args, (
"Gym environments don't support args. Use kwargs instead.")
self._Serializable__initialize(locals())
self.normalize = normalize
self.unwrap_time_limit = unwrap_time_limit
super(GymAdapter, self).__init__(domain, task, *args, **kwargs)
if env is None:
assert (domain is not None and task is not None), (domain, task)
env_id = f"{domain}-{task}"
env = gym.envs.make(env_id, **kwargs)
else:
assert domain is None and task is None, (domain, task)
if isinstance(env, wrappers.TimeLimit) and unwrap_time_limit:
# Remove the TimeLimit wrapper that sets 'done = True' when
# the time limit specified for each environment has been passed and
# therefore the environment is not Markovian (terminal condition
# depends on time rather than state).
env = env.env
if isinstance(env.observation_space, spaces.Dict):
observation_keys = (observation_keys
or tuple(env.observation_space.spaces.keys()))
self.observation_keys = observation_keys
if normalize:
env = NormalizeActionWrapper(env)
self._env = env
def __init__(self,
domain,
task,
*args,
env=None,
normalize=True,
observation_keys=None,
unwrap_time_limit=True,
**kwargs):
assert not args, (
"Gym environments don't support args. Use kwargs instead.")
self.normalize = normalize
self.observation_keys = observation_keys
self.unwrap_time_limit = unwrap_time_limit
self.stacks = 1
self.stacking_axis = 0
self._Serializable__initialize(locals())
super(GymAdapter, self).__init__(domain, task, *args, **kwargs)
if env is None:
assert (domain is not None and task is not None), (domain, task)
env_id = f"{domain}-{task}"
env = gym.envs.make(env_id, **kwargs)
#env_id = f""
#env = gym.make("Safexp-PointGoal1-v0")
else:
assert domain is None and task is None, (domain, task)
env_id = 'custom'
if isinstance(env, wrappers.TimeLimit) and unwrap_time_limit:
# Remove the TimeLimit wrapper that sets 'done = True' when
# the time limit specified for each environment has been passed and
# therefore the environment is not Markovian (terminal condition
# depends on time rather than state).
env = env.env
if isinstance(env.observation_space, spaces.Dict):
observation_keys = (
observation_keys or list(env.observation_space.spaces.keys()))
if normalize:
env = NormalizeActionWrapper(env)
#### --- specifically for safety_gym wrappring --- ###
if env_id in SAFETY_WRAPPER_IDS:
dirname, _ = os.path.split(os.path.abspath(__file__))
#### load config file
with open(f'{dirname}/../gym/safety_gym/configs/{env_id}_config.json', 'r') as fp:
config = json.load(fp)
fp.close()
# with open(f'{dirname}/../gym/safety_gym/add_configs/{env_id}_add_config.json', 'r') as fp:
# add_config = json.load(fp)
# fp.close()
env = Engine(config)
# env = SAFETY_WRAPPER_IDS[env_id](env)
#### additional config info like stacking etc.
# for k in add_config.keys():
# self.safeconfig[k] = add_config[k]
#### dump config file to current data dir
with open(f'{env_id}_config.json', 'w') as fp:
json.dump(config, fp)
fp.close()
####
### adding unserializable additional info after dumping (lol)
# self.obs_indices = env.obs_indices
# self.safeconfig['obs_indices'] = self.obs_indices
### stack env
self.stacks = config.get('stacks', 1) ### for convenience
self.stacking_axis = config.get('stacking_axis',0)
if self.stacks>1:
env = DummyVecEnv([lambda:env])
#env = VecNormalize(env) doesn't work at all for some reason
env = VecFrameStack(env, self.stacks)
#### --- end specifically for safety_gym --- ###
self._env = env
def __init__(self,
domain,
task,
*args,
env=None,
normalize=True,
observation_keys=(),
goal_keys=(),
unwrap_time_limit=True,
pixel_wrapper_kwargs=None,
**kwargs):
assert not args, (
"Gym environments don't support args. Use kwargs instead.")
self.normalize = normalize
self.unwrap_time_limit = unwrap_time_limit
super(GymAdapter, self).__init__(domain,
task,
*args,
goal_keys=goal_keys,
**kwargs)
if env is None:
assert (domain is not None and task is not None), (domain, task)
env_id = f"{domain}-{task}"
env = gym.envs.make(env_id, **kwargs)
self._env_kwargs = kwargs
else:
assert not kwargs
assert domain is None and task is None, (domain, task)
if isinstance(env, wrappers.TimeLimit) and unwrap_time_limit:
# Remove the TimeLimit wrapper that sets 'done = True' when
# the time limit specified for each environment has been passed and
# therefore the environment is not Markovian (terminal condition
# depends on time rather than state).
env = env.env
if normalize:
env = NormalizeActionWrapper(env)
if pixel_wrapper_kwargs is not None:
env = PixelObservationWrapper(env, **pixel_wrapper_kwargs)
self._env = env
if isinstance(self._env.observation_space, spaces.Dict):
dict_observation_space = self._env.observation_space
self.observation_keys = (observation_keys or
(*self.observation_space.spaces.keys(), ))
elif isinstance(self._env.observation_space, spaces.Box):
dict_observation_space = spaces.Dict(
OrderedDict(((DEFAULT_OBSERVATION_KEY,
self._env.observation_space), )))
self.observation_keys = (DEFAULT_OBSERVATION_KEY, )
self._observation_space = type(dict_observation_space)([
(name, copy.deepcopy(space))
for name, space in dict_observation_space.spaces.items()
if name in self.observation_keys
])
if len(self._env.action_space.shape) > 1:
raise NotImplementedError(
"Shape of the action space ({}) is not flat, make sure to"
" check the implemenation.".format(self._env.action_space))
self._action_space = self._env.action_space
