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Reverb trajectory sequence observer.
Inherits From: ReverbAddTrajectoryObserver
tf_agents.replay_buffers.reverb_utils.ReverbTrajectorySequenceObserver(
py_client: tf_agents.typing.types.ReverbClient
,
table_name: Union[Text, Sequence[Text]],
sequence_length: int,
stride_length: int = 1,
priority: Union[float, int] = 1
)
This is equivalent to ReverbAddTrajectoryObserver but sequences are not cut when a boundary trajectory is seen. This allows for sequences to be sampled with boundaries anywhere in the sequence rather than just at the end.
Consider using this observer when you want to create training experience that can encompass any subsequence of the observed trajectories.
Args | |
---|---|
py_client
|
Python client for the reverb replay server. |
table_name
|
The table name(s) where samples will be written to. |
sequence_length
|
The sequence_length used to write to the given table. |
stride_length
|
The integer stride for the sliding window for overlapping
sequences. The default value of 1 creates an item for every
window. Using L = sequence_length this means items are created for
times {0, 1, .., L-1}, {1, 2, .., L}, ... . In contrast,
stride_length = L will create an item only for disjoint windows
{0, 1, ..., L-1}, {L, ..., 2 * L - 1}, ... .
|
priority
|
Initial priority for new samples in the RB. |
Methods
close
close()
Closes the writer of the observer.
open
open()
Open the writer of the observer.
reset
reset()
Resets the state of the observer.
No data observed before the reset will be pushed to the RB.
__call__
__call__(
trajectory
)
Writes the trajectory into the underlying replay buffer.
Allows trajectory to be a flattened trajectory. No batch dimension allowed.
Args | |
---|---|
trajectory
|
The trajectory to be written which could be (possibly nested) trajectory object or a flattened version of a trajectory. It assumes there is no batch dimension. |