garage.torch.embeddings¶
PyTorch embedding modules for meta-learning algorithms.
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class
MLPEncoder(input_dim, output_dim, hidden_sizes, hidden_nonlinearity=F.relu, hidden_w_init=nn.init.xavier_normal_, hidden_b_init=nn.init.zeros_, output_nonlinearity=None, output_w_init=nn.init.xavier_normal_, output_b_init=nn.init.zeros_, layer_normalization=False)¶ Bases:
garage.torch.modules.MLPModule,garage.np.embeddings.Encoder
This MLP network encodes context of RL tasks.
Context is stored in the terms of observation, action, and reward, and this network uses an MLP module for encoding it.
- Parameters
input_dim (int) – Dimension of the network input.
output_dim (int) – Dimension of the network output.
hidden_sizes (list[int]) – Output dimension of dense layer(s). For example, (32, 32) means this MLP consists of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable or torch.nn.Module) – Activation function for intermediate dense layer(s). It should return a torch.Tensor.Set it to None to maintain a linear activation.
hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a torch.Tensor.
hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a torch.Tensor.
output_nonlinearity (callable or torch.nn.Module) – Activation function for output dense layer. It should return a torch.Tensor. Set it to None to maintain a linear activation.
output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a torch.Tensor.
output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a torch.Tensor.
layer_normalization (bool) – Bool for using layer normalization or not.
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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)¶ 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.