def clone_wrapped(env):
"""
Clone Serializable AsaEnv wrapped into multiple other environment wrappers.
This performs Serializable.clone on inner env and all its wrappers, if possible.
Supported wrappers:
- TfEnv: no cloning needed, only wrap in new instance
- NormalizedEnv: clone and wrap
- other Serializable: clone and wrap, display warning
- other non-Serializable: wrap in new instance, display warning
:param env: AsaEnv wrapped in multiple other environment wrappers
"""
# Unwrap
stack = []
while not isinstance(env, AsaEnv):
stack.append((type(env), env))
env = env.env
# Clone inner env
new_env = Serializable.clone(env)
# Re-wrap, cloning wrappers on the way
while stack:
wrapper_cls, wrapper_env = stack.pop()
if wrapper_cls is TfEnv:
new_env = TfEnv(new_env)
elif wrapper_cls is NormalizedEnv:
# WARNING: obs_mean and obs_var are not copied to original env after skill training!
new_env = Serializable.clone(wrapper_env, env=new_env)
elif isinstance(wrapper_env, Serializable):
new_env = Serializable.clone(wrapper_env, env=new_env)
warn_once(
'AsaEnv: clone_wrapped performed on unknown Serializable wrapper "{}". '
'Wrapper was cloned and applied.'.format(wrapper_cls))
else:
new_env = wrapper_cls(env=new_env)
warn_once(
'AsaEnv: clone_wrapped performed on unknown non-Serializable wrapper "{}". '
'Wrapper was initiated with default parameters and applied.'
.format(wrapper_cls))
return new_env
def create_new_skill(self, end_obss):
"""
Create new untrained skill and add it to skills list, along with its stopping function.
:return: new skill policy and skill ID (index of the skill)
:rtype: tuple(garage.policies.base.Policy, int)
"""
new_skill_id = len(self.skill_policies)
new_skill_pol = Serializable.clone(
obj=self.skill_policy_prototype,
name='{}Skill{}'.format(
type(self.skill_policy_prototype).__name__, new_skill_id))
self.skill_policies.append(new_skill_pol)
self._skills_end_obss.append(np.copy(end_obss))
unique_end_obss = np.unique(self._skills_end_obss[new_skill_id],
axis=0)
self._skill_stop_functions.append(
# lambda path: path['observations'][-1] in self._skills_end_obss[new_skill_id]
lambda path:
(path['observations'][-1] == unique_end_obss).all(axis=1).any())
return new_skill_pol, new_skill_id