def write_to_shared_memory(index, value, shared_memory, space):
"""Write the observation of a single environment into shared memory.
Parameters
----------
index : int
Index of the environment (must be in `[0, num_envs)`).
value : sample from `space`
Observation of the single environment to write to shared memory.
shared_memory : dict, tuple, or `multiprocessing.Array` instance
Shared object across processes. This contains the observations from the
vectorized environment. This object is created with `create_shared_memory`.
space : `gym.spaces.Space` instance
Observation space of a single environment in the vectorized environment.
Returns
-------
`None`
"""
if isinstance(space, _BaseGymSpaces):
write_base_to_shared_memory(index, value, shared_memory, space)
elif isinstance(space, Tuple):
write_tuple_to_shared_memory(index, value, shared_memory, space)
elif isinstance(space, Dict):
write_dict_to_shared_memory(index, value, shared_memory, space)
else:
raise CustomSpaceError(
"Cannot write to a shared memory for space with "
"type `{}`. Shared memory only supports "
"default Gym spaces (e.g. `Box`, `Tuple`, "
"`Dict`, etc...), and does not support custom "
"Gym spaces.".format(type(space)))
def create_shared_memory(space, n=1, ctx=mp):
"""Create a shared memory object, to be shared across processes. This
eventually contains the observations from the vectorized environment.
Parameters
----------
space : `gym.spaces.Space` instance
Observation space of a single environment in the vectorized environment.
n : int
Number of environments in the vectorized environment (i.e. the number
of processes).
ctx : `multiprocessing` context
Context for multiprocessing.
Returns
-------
shared_memory : dict, tuple, or `multiprocessing.Array` instance
Shared object across processes.
"""
if isinstance(space, _BaseGymSpaces):
return create_base_shared_memory(space, n=n, ctx=ctx)
elif isinstance(space, Tuple):
return create_tuple_shared_memory(space, n=n, ctx=ctx)
elif isinstance(space, Dict):
return create_dict_shared_memory(space, n=n, ctx=ctx)
else:
raise CustomSpaceError("Cannot create a shared memory for space with "
"type `{}`. Shared memory only supports "
"default Gym spaces (e.g. `Box`, `Tuple`, "
"`Dict`, etc...), and does not support custom "
"Gym spaces.".format(type(space)))
def read_from_shared_memory(space: Space,
shared_memory: Union[dict, tuple, mp.Array],
n: int = 1) -> Union[dict, tuple, np.ndarray]:
"""Read the batch of observations from shared memory as a numpy array.
..notes::
The numpy array objects returned by `read_from_shared_memory` shares the
memory of `shared_memory`. Any changes to `shared_memory` are forwarded
to `observations`, and vice-versa. To avoid any side-effect, use `np.copy`.
Args:
space: Observation space of a single environment in the vectorized environment.
shared_memory: Shared object across processes. This contains the observations from the vectorized environment.
This object is created with `create_shared_memory`.
n: Number of environments in the vectorized environment (i.e. the number of processes).
Returns:
Batch of observations as a (possibly nested) numpy array.
"""
raise CustomSpaceError(
"Cannot read from a shared memory for space with "
f"type `{type(space)}`. Shared memory only supports "
"default Gym spaces (e.g. `Box`, `Tuple`, "
"`Dict`, etc...), and does not support custom "
"Gym spaces.")
def read_from_shared_memory(space, shared_memory, n=1):
"""Read the batch of observations from shared memory as a numpy array.
Parameters
----------
shared_memory : dict, tuple, or `multiprocessing.Array` instance
Shared object across processes. This contains the observations from the
vectorized environment. This object is created with `create_shared_memory`.
space : `gym.spaces.Space` instance
Observation space of a single environment in the vectorized environment.
n : int
Number of environments in the vectorized environment (i.e. the number
of processes).
Returns
-------
observations : dict, tuple or `np.ndarray` instance
Batch of observations as a (possibly nested) numpy array.
Notes
-----
The numpy array objects returned by `read_from_shared_memory` shares the
memory of `shared_memory`. Any changes to `shared_memory` are forwarded
to `observations`, and vice-versa. To avoid any side-effect, use `np.copy`.
"""
raise CustomSpaceError("Cannot read from a shared memory for space with "
"type `{}`. Shared memory only supports "
"default Gym spaces (e.g. `Box`, `Tuple`, "
"`Dict`, etc...), and does not support custom "
"Gym spaces.".format(type(space)))
def write_to_shared_memory(
space: Space,
index: int,
value: np.ndarray,
shared_memory: Union[dict, tuple, mp.Array],
):
"""Write the observation of a single environment into shared memory.
Args:
space: Observation space of a single environment in the vectorized environment.
index: Index of the environment (must be in `[0, num_envs)`).
value: Observation of the single environment to write to shared memory.
shared_memory: Shared object across processes. This contains the observations from the vectorized environment. This object is created with `create_shared_memory`.
"""
raise CustomSpaceError(
"Cannot write to a shared memory for space with "
f"type `{type(space)}`. Shared memory only supports "
"default Gym spaces (e.g. `Box`, `Tuple`, "
"`Dict`, etc...), and does not support custom "
"Gym spaces.")
def create_shared_memory(space: Space,
n: int = 1,
ctx=mp) -> Union[dict, tuple, mp.Array]:
"""Create a shared memory object, to be shared across processes.
This eventually contains the observations from the vectorized environment.
Args:
space: Observation space of a single environment in the vectorized environment.
n: Number of environments in the vectorized environment (i.e. the number of processes).
ctx: The multiprocess module
Returns:
shared_memory for the shared object across processes.
"""
raise CustomSpaceError(
"Cannot create a shared memory for space with "
f"type `{type(space)}`. Shared memory only supports "
"default Gym spaces (e.g. `Box`, `Tuple`, "
"`Dict`, etc...), and does not support custom "
"Gym spaces.")
def _iterate_custom(space, items):
raise CustomSpaceError(f"Unable to iterate over {items}, since {space} "
"is a custom `gym.Space` instance (i.e. not one of "
"`Box`, `Dict`, etc...).")