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 TimeStep
s in a sequence will
have `StepType.MID.
Attributes:
Name | Type | Description |
---|---|---|
step_type |
StepType |
A |
reward |
Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number] |
A scalar, NumPy array, nested dict, list or tuple of rewards; or
|
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 |
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. |