Types

types

StepType (int8)

Defines the status of a TimeStep within a sequence.

First: 0 Mid: 1 Last: 2

TimeStep (Generic, Mapping) dataclass

Copied from dm_env.TimeStep with the goal of making it a Jax Type. The original dm_env.TimeStep is not a Jax type because inheriting a namedtuple is not treated as a valid Jax type (https://github.com/google/jax/issues/806).

A TimeStep contains the data emitted by an environment at each step of interaction. A TimeStep holds a step_type, an observation (typically a NumPy array or a dict or list of arrays), and an associated reward and discount.

The first TimeStep in a sequence will have StepType.FIRST. The final TimeStep will have StepType.LAST. All other TimeSteps in a sequence will have `StepType.MID.

Attributes:

Name	Type	Description
step_type	StepType	A StepType enum value.
reward	Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number]	A scalar, NumPy array, nested dict, list or tuple of rewards; or None if step_type is StepType.FIRST, i.e. at the start of a sequence.
discount	Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number]	A scalar, NumPy array, nested dict, list or tuple of discount values in the range [0, 1], or None if step_type is StepType.FIRST, i.e. at the start of a sequence.
observation	~Observation	A NumPy array, or a nested dict, list or tuple of arrays. Scalar values that can be cast to NumPy arrays (e.g. Python floats) are also valid in place of a scalar array.
extras	Dict	environment metric(s) or information returned by the environment but not observed by the agent (hence not in the observation). For example, it could be whether an invalid action was taken. In most environments, extras is an empty dictionary.

step_type: StepType dataclass-field

reward: Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number] dataclass-field

discount: Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number] dataclass-field

observation: ~Observation dataclass-field

extras: Dict dataclass-field

__eq__(self, other) special

__init__(self, step_type: StepType, reward: Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number], discount: Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number], observation: ~Observation, extras: Dict = <factory>) -> None special

__repr__(self) special

__getitem__(self, x) special

__len__(self) special

__iter__(self) special

first(self) -> Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number]

mid(self) -> Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number]

last(self) -> Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number]

from_tuple(args)

to_tuple(self)

replace(self, **kwargs)

__getstate__(self) special

__setstate__(self, state) special

restart(observation: ~Observation, extras: Optional[Dict] = None, shape: Union[int, Sequence[int]] = ()) -> TimeStep

Returns a TimeStep with step_type set to StepType.FIRST.

Parameters:

Name	Type	Description	Default
observation	~Observation	array or tree of arrays.	required
extras	Optional[Dict]	environment metric(s) or information returned by the environment but not observed by the agent (hence not in the observation). For example, it could be whether an invalid action was taken. In most environments, extras is None.	None
shape	Union[int, Sequence[int]]	optional parameter to specify the shape of the rewards and discounts. Allows multi-agent environment compatibility. Defaults to () for scalar reward and discount.	()

Returns:

Type	Description
TimeStep	TimeStep identified as a reset.

transition(reward: Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number], observation: ~Observation, discount: Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number] = None, extras: Optional[Dict] = None, shape: Union[int, Sequence[int]] = ()) -> TimeStep

Returns a TimeStep with step_type set to StepType.MID.

Parameters:

Name	Type	Description	Default
reward	Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number]	array.	required
observation	~Observation	array or tree of arrays.	required
discount	Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number]	array.	None
extras	Optional[Dict]	environment metric(s) or information returned by the environment but not observed by the agent (hence not in the observation). For example, it could be whether an invalid action was taken. In most environments, extras is None.	None
shape	Union[int, Sequence[int]]	optional parameter to specify the shape of the rewards and discounts. Allows multi-agent environment compatibility. Defaults to () for scalar reward and discount.	()

Returns:

Type	Description
TimeStep	TimeStep identified as a transition.

termination(reward: Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number], observation: ~Observation, extras: Optional[Dict] = None, shape: Union[int, Sequence[int]] = ()) -> TimeStep

Returns a TimeStep with step_type set to StepType.LAST.

Parameters:

Name	Type	Description	Default
reward	Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number]	array.	required
observation	~Observation	array or tree of arrays.	required
extras	Optional[Dict]	environment metric(s) or information returned by the environment but not observed by the agent (hence not in the observation). For example, it could be whether an invalid action was taken. In most environments, extras is None.	None
shape	Union[int, Sequence[int]]	optional parameter to specify the shape of the rewards and discounts. Allows multi-agent environment compatibility. Defaults to () for scalar reward and discount.	()

Returns:

Type	Description
TimeStep	TimeStep identified as the termination of an episode.

truncation(reward: Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number], observation: ~Observation, discount: Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number] = None, extras: Optional[Dict] = None, shape: Union[int, Sequence[int]] = ()) -> TimeStep

Returns a TimeStep with step_type set to StepType.LAST.

Parameters:

Name	Type	Description	Default
reward	Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number]	array.	required
observation	~Observation	array or tree of arrays.	required
discount	Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number]	array.	None
extras	Optional[Dict]	environment metric(s) or information returned by the environment but not observed by the agent (hence not in the observation). For example, it could be whether an invalid action was taken. In most environments, extras is None.	None
shape	Union[int, Sequence[int]]	optional parameter to specify the shape of the rewards and discounts. Allows multi-agent environment compatibility. Defaults to () for scalar reward and discount.	()

Returns:

Type	Description
TimeStep	TimeStep identified as the truncation of an episode.

get_valid_dtype(dtype: Union[numpy.dtype, type]) -> dtype

Cast a dtype taking into account the user type precision. E.g., if 64 bit is not enabled, jnp.dtype(jnp.float_) is still float64. By passing the given dtype through jnp.empty we get the supported dtype of float32.

Parameters:

Name	Type	Description	Default
dtype	Union[numpy.dtype, type]	jax numpy dtype or string specifying the array dtype.	required

Returns:

Type	Description
dtype	dtype converted to the correct type precision.

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Last update: 2024-03-29