garage.np.embeddings.encoder module¶

Base class for context encoder.

class Encoder[source]¶

Bases: abc.ABC

Base class of context encoders for training meta-RL algorithms.

input_dim¶

Dimension of the encoder input.

Type:	int

output_dim¶

Dimension of the encoder output (embedding).

Type:	int

reset(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.

spec¶

Input and output space.

Type:	garage.InOutSpec

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.

distribution¶

Embedding distribution.

Type:	object