garage.torch.q_functions.discrete_cnn_q_function
¶
Discrete CNN Q Function.
- class DiscreteCNNQFunction(env_spec, image_format, *, kernel_sizes, hidden_channels, strides, hidden_sizes=(32, 32), cnn_hidden_nonlinearity=torch.nn.ReLU, mlp_hidden_nonlinearity=torch.nn.ReLU, hidden_w_init=nn.init.xavier_uniform_, hidden_b_init=nn.init.zeros_, paddings=0, padding_mode='zeros', max_pool=False, pool_shape=None, pool_stride=1, output_nonlinearity=None, output_w_init=nn.init.xavier_uniform_, output_b_init=nn.init.zeros_, layer_normalization=False)¶
Bases:
torch.nn.Module
Discrete CNN Q Function.
A Q network that estimates Q values of all possible discrete actions. It is constructed using a CNN followed by one or more fully-connected layers.
- Parameters
env_spec (EnvSpec) – Environment specification.
image_format (str) – Either ‘NCHW’ or ‘NHWC’. Should match the input specification. Gym uses NHWC by default, but PyTorch uses NCHW by default.
kernel_sizes (tuple[int]) – Dimension of the conv filters. For example, (3, 5) means there are two convolutional layers. The filter for first layer is of dimension (3 x 3) and the second one is of dimension (5 x 5).
strides (tuple[int]) – The stride of the sliding window. For example, (1, 2) means there are two convolutional layers. The stride of the filter for first layer is 1 and that of the second layer is 2.
hidden_channels (tuple[int]) – Number of output channels for CNN. For example, (3, 32) means there are two convolutional layers. The filter for the first conv layer outputs 3 channels and the second one outputs 32 channels.
hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
mlp_hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s) in the MLP. It should return a torch.Tensor. Set it to None to maintain a linear activation.
cnn_hidden_nonlinearity (callable) – Activation function for intermediate CNN 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.
paddings (tuple[int]) – Zero-padding added to both sides of the input
padding_mode (str) – The type of padding algorithm to use, either ‘SAME’ or ‘VALID’.
max_pool (bool) – Bool for using max-pooling or not.
pool_shape (tuple[int]) – Dimension of the pooling layer(s). For example, (2, 2) means that all the pooling layers are of the same shape (2, 2).
pool_stride (tuple[int]) – The strides of the pooling layer(s). For example, (2, 2) means that all the pooling layers have strides (2, 2).
output_nonlinearity (callable) – 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.
- forward(observations)¶
Return Q-value(s).
- Parameters
observations (np.ndarray) – observations of shape :math: (N, O*).
- Returns
Output value
- Return type
torch.Tensor