def from_tensor(self, *, tensor, batched, name='TensorSpec.from_tensor'):
if not isinstance(tensor, TensorDict):
raise TensorforceError.type(name=name, argument='tensor', dtype=type(tensor))
# TODO: improve exception message to include invalid keys
if set(tensor) != set(self):
raise TensorforceError.value(name=name, argument='tensor', value=tensor)
value = ArrayDict()
for name, spec in super(NestedDict, self).items():
value[name] = spec.from_tensor(tensor=tensor[name], batched=batched, name=name)
return value
def to_tensor(self, *, value, batched, recover_empty=False, name='TensorSpec.to_tensor'):
if not isinstance(value, ArrayDict):
raise TensorforceError.type(name=name, argument='value', dtype=type(value))
# TODO: improve exception message to include invalid keys
if set(value) != set(self):
raise TensorforceError.value(name=name, argument='value', value=value)
tensor = TensorDict()
for name, spec in super(NestedDict, self).items():
if recover_empty and name not in value:
assert not isinstance(spec, self.value_type) and len(spec) == 0
tensor[name] = spec.to_tensor(
value=ArrayDict(), batched=batched, recover_empty=recover_empty
)
else:
tensor[name] = spec.to_tensor(
value=value[name], batched=batched, recover_empty=recover_empty
)
return tensor
def internals_init(self):
return ArrayDict()