garage.torch.policies.discrete_cnn_policy

Discrete CNN Policy.

class DiscreteCNNPolicy(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, name='DiscreteCNNPolicy')

Bases: garage.torch.policies.stochastic_policy.StochasticPolicy

Inheritance diagram of garage.torch.policies.discrete_cnn_policy.DiscreteCNNPolicy

DiscreteCNNPolicy.

A policy that contains a CNN and a MLP to make prediction based on a discrete distribution.

Parameters
  • env_spec (EnvSpec) – Environment specification.

  • image_format (str) – Either ‘NCHW’ or ‘NHWC’. Should match env_spec. 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

  • 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 have 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.

  • name (str) – Name of policy.

forward(self, observations)

Compute the action distributions from the observations.

Parameters

observations (torch.Tensor) – Batch of observations of shape \((N, O)\). Observations should be flattened even if they are images as the underlying Q network handles unflattening.

Returns

Batch distribution of actions. dict[str, torch.Tensor]: Additional agent_info, as torch Tensors.

Do not need to be detached, and can be on any device.

Return type

torch.distributions.Distribution

get_action(self, observation)

Get a single action given an observation.

Parameters

observation (np.ndarray) – Observation from the environment. Shape is \(env_spec.observation_space\).

Returns

  • np.ndarray: Predicted action. Shape is

    \(env_spec.action_space\).

  • dict:
    • np.ndarray[float]: Mean of the distribution

    • np.ndarray[float]: Standard deviation of logarithmic

      values of the distribution.

Return type

tuple

get_actions(self, observations)

Get actions given observations.

Parameters

observations (np.ndarray) – Observations from the environment. Shape is \(batch_dim \bullet env_spec.observation_space\).

Returns

  • np.ndarray: Predicted actions.

    \(batch_dim \bullet env_spec.action_space\).

  • dict:
    • np.ndarray[float]: Mean of the distribution.

    • np.ndarray[float]: Standard deviation of logarithmic

      values of the distribution.

Return type

tuple

get_param_values(self)

Get the parameters to the policy.

This method is included to ensure consistency with TF policies.

Returns

The parameters (in the form of the state dictionary).

Return type

dict

set_param_values(self, state_dict)

Set the parameters to the policy.

This method is included to ensure consistency with TF policies.

Parameters

state_dict (dict) – State dictionary.

property name(self)

Name of policy.

Returns

Name of policy

Return type

str

reset(self, do_resets=None)

Reset the policy.

This is effective only to recurrent policies.

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, i.e. batch size.

Parameters

do_resets (numpy.ndarray) – Bool array indicating which states to be reset.

property env_spec(self)

Policy environment specification.

Returns

Environment specification.

Return type

garage.EnvSpec

property observation_space(self)

Observation space.

Returns

The observation space of the environment.

Return type

akro.Space

property action_space(self)

Action space.

Returns

The action space of the environment.

Return type

akro.Space