def _space_contains(self, space: gym.Space, x: MultiEnvDict) -> bool:
"""Check if the given space contains the observations of x.
Args:
space: The space to if x's observations are contained in.
x: The observations to check.
Returns:
True if the observations of x are contained in space.
"""
agents = set(self.get_agent_ids())
for multi_agent_dict in x.values():
for agent_id, obs in multi_agent_dict.items():
# this is for the case where we have a single agent
# and we're checking a Vector env thats been converted to
# a BaseEnv
if agent_id == _DUMMY_AGENT_ID:
if not space.contains(obs):
return False
# for the MultiAgent env case
elif (agent_id
not in agents) or (not space[agent_id].contains(obs)):
return False
return True
def _space_contains(space: gym.Space, x: MultiEnvDict) -> bool:
"""Check if the given space contains the observations of x.
Args:
space: The space to if x's observations are contained in.
x: The observations to check.
Note: With vector envs, we can process the raw observations
and ignore the agent ids and env ids, since vector envs'
sub environements are guaranteed to be the same
Returns:
True if the observations of x are contained in space.
"""
for _, multi_agent_dict in x.items():
for _, element in multi_agent_dict.items():
if not space.contains(element):
return False
return True
def _gym_space_contains(space: gym.Space, x: Any) -> None:
"""Strengthened version of gym.Space.contains.
Giving more diagnostic information on why validation fails.
Throw exception rather than returning true or false.
"""
if isinstance(space, spaces.Dict):
if not isinstance(x, dict) or len(x) != len(space):
raise GymSpaceValidationError(
"Sample must be a dict with same length as space.", space, x)
for k, subspace in space.spaces.items():
if k not in x:
raise GymSpaceValidationError(f"Key {k} not found in sample.",
space, x)
try:
_gym_space_contains(subspace, x[k])
except GymSpaceValidationError as e:
raise GymSpaceValidationError(
f"Subspace of key {k} validation error.", space, x) from e
elif isinstance(space, spaces.Tuple):
if isinstance(x, (list, np.ndarray)):
x = tuple(
x) # Promote list and ndarray to tuple for contains check
if not isinstance(x, tuple) or len(x) != len(space):
raise GymSpaceValidationError(
"Sample must be a tuple with same length as space.", space, x)
for i, (subspace, part) in enumerate(zip(space, x)):
try:
_gym_space_contains(subspace, part)
except GymSpaceValidationError as e:
raise GymSpaceValidationError(
f"Subspace of index {i} validation error.", space,
x) from e
else:
if not space.contains(x):
raise GymSpaceValidationError("Validation error reported by gym.",
space, x)
def __init__(self, ob_space: gym.Space, ac_space: gym.Space, fixed_val: np.ndarray):
super().__init__(ob_space, ac_space)
self.fixed_val = fixed_val
if not ac_space.contains(fixed_val): # pragma: no cover
raise ValueError(f"fixed_val = '{fixed_val}' not contained in ac_space = '{ac_space}'")