def update_env(self, env_update):
"""Update the environments.
If passed a list (*inside* this list passed to the Sampler itself),
distributes the environments across the "vectorization" dimension.
Args:
env_update(Environment or EnvUpdate or None): The environment to
replace the existing env with. Note that other implementations
of `Worker` may take different types for this parameter.
Raises:
TypeError: If env_update is not one of the documented types.
ValueError: If the wrong number of updates is passed.
"""
if isinstance(env_update, list):
if len(env_update) != self._n_envs:
raise ValueError('If separate environments are passed for '
'each worker, there must be exactly n_envs '
'({}) environments, but received {} '
'environments.'.format(
self._n_envs, len(env_update)))
elif env_update is not None:
env_update = [
copy.deepcopy(env_update) for _ in range(self._n_envs)
]
if env_update:
for env_index, env_up in enumerate(env_update):
self._envs[env_index], up = _apply_env_update(
self._envs[env_index], env_up)
self._needs_env_reset |= up
def update_env(self, env_update):
"""Use any non-None env_update as a new environment.
A simple env update function. If env_update is not None, it should be
the complete new environment.
This allows changing environments by passing the new environment as
`env_update` into `obtain_samples`.
Args:
env_update(Environment or EnvUpdate or None): The environment to
replace the existing env with. Note that other implementations
of `Worker` may take different types for this parameter.
Raises:
TypeError: If env_update is not one of the documented types.
"""
self.env, _ = _apply_env_update(self.env, env_update)