garage.np.embeddings
¶
Embedding encoders and decoders which use NumPy as a numerical backend.
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class
Encoder
[source]¶ Bases:
abc.ABC
Base class of context encoders for training meta-RL algorithms.
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property
spec
(self)¶ garage.InOutSpec: Input and output space.
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property
input_dim
(self)¶ int: Dimension of the encoder input.
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property
output_dim
(self)¶ int: Dimension of the encoder output (embedding).
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reset
(self, do_resets=None)[source]¶ Reset the encoder.
This is effective only to recurrent encoder. do_resets is effective only to vectoried encoder.
For a vectorized encoder, do_resets is an array of boolean indicating which internal states to be reset. The length of do_resets should be equal to the length of inputs.
- Parameters
do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
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property
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class
StochasticEncoder
[source]¶ Bases:
garage.np.embeddings.encoder.Encoder
An stochastic context encoders.
An stochastic encoder maps an input to a distribution, but not a deterministic vector.
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property
distribution
(self)¶ object: Embedding distribution.
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property
spec
(self)¶ garage.InOutSpec: Input and output space.
-
property
input_dim
(self)¶ int: Dimension of the encoder input.
-
property
output_dim
(self)¶ int: Dimension of the encoder output (embedding).
-
reset
(self, do_resets=None)¶ Reset the encoder.
This is effective only to recurrent encoder. do_resets is effective only to vectoried encoder.
For a vectorized encoder, do_resets is an array of boolean indicating which internal states to be reset. The length of do_resets should be equal to the length of inputs.
- Parameters
do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
-
property