terminated_or_truncated

torchrl.envs.terminated_or_truncated(data: TensorDictBase, full_done_spec: torchrl.data.tensor_specs.TensorSpec | None = None, key: str = '_reset', write_full_false: bool = False) → bool

Reads the done / terminated / truncated keys within a tensordict, and writes a new tensor where the values of both signals are aggregated.

The modification occurs in-place within the TensorDict instance provided. This function can be used to compute the “_reset” signals in batched or multiagent settings, hence the default name of the output key.

Parameters:

• data (TensorDictBase) – the input data, generally resulting from a call to step().

• full_done_spec (TensorSpec, optional) – the done_spec from the env, indicating where the done leaves have to be found. If not provided, the default "done", "terminated" and "truncated" entries will be searched for in the data.

• key (NestedKey, optional) –

  where the aggregated result should be written. If None, then the function will not write any key but just output whether any of the done values was true. .. note:: if a value is already present for the key entry,

  the previous value will prevail and no update will be achieved.

• write_full_false (bool, optional) – if True, the reset keys will be written even if the output is False (ie, no done is True in the provided data structure). Defaults to False.

Returns: a boolean value indicating whether any of the done states found in the data

contained a True.

Examples

>>> from torchrl.data.tensor_specs import Categorical
>>> from tensordict import TensorDict
>>> spec = Composite(
...     done=Categorical(2, dtype=torch.bool),
...     truncated=Categorical(2, dtype=torch.bool),
...     nested=Composite(
...         done=Categorical(2, dtype=torch.bool),
...         truncated=Categorical(2, dtype=torch.bool),
...     )
... )
>>> data = TensorDict({
...     "done": True, "truncated": False,
...     "nested": {"done": False, "truncated": True}},
...     batch_size=[]
... )
>>> data = _terminated_or_truncated(data, spec)
>>> print(data["_reset"])
tensor(True)
>>> print(data["nested", "_reset"])
tensor(True)