garage.tf.policies.categorical_cnn_policy¶
Categorical CNN Policy.
A policy represented by a Categorical distribution which is parameterized by a convolutional neural network (CNN) followed a multilayer perceptron (MLP).
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class
CategoricalCNNPolicy(env_spec, filters, strides, padding, name='CategoricalCNNPolicy', hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=tf.nn.softmax, output_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_b_init=tf.zeros_initializer(), layer_normalization=False)¶ Bases:
garage.tf.models.CategoricalCNNModel,garage.tf.policies.policy.Policy
CategoricalCNNPolicy.
A policy that contains a CNN and a MLP to make prediction based on a categorical distribution.
It only works with akro.Discrete action space.
- Parameters
env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
filters (Tuple[Tuple[int, Tuple[int, int]], ..]) – Number and dimension of filters. For example, ((3, (3, 5)), (32, (3, 3))) means there are two convolutional layers. The filter for the first layer have 3 channels and its shape is (3 x 5), while the filter for the second layer have 32 channels and its shape is (3 x 3).
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.
padding (str) – The type of padding algorithm to use, either ‘SAME’ or ‘VALID’.
name (str) – Policy name, also the variable scope of the policy.
hidden_sizes (list[int]) – Output dimension of dense layer(s). For example, (32, 32) means the MLP of this policy consists of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a tf.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 tf.Tensor.
hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor.
output_nonlinearity (callable) – Activation function for output dense layer. It should return a tf.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 tf.Tensor.
output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor.
layer_normalization (bool) – Bool for using layer normalization or not.
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property
input_dim(self)¶ int: Dimension of the policy input.
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get_action(self, observation)¶ Return a single action.
- Parameters
observation (numpy.ndarray) – Observations.
- Returns
Action given input observation. dict(numpy.ndarray): Distribution parameters.
- Return type
-
get_actions(self, observations)¶ Return multiple actions.
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property
env_spec(self)¶ Policy environment specification.
- Returns
Environment specification.
- Return type
garage.EnvSpec
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clone(self, name)¶ Return a clone of the policy.
It copies the configuration of the primitive and also the parameters.
- Parameters
name (str) – Name of the newly created policy. It has to be different from source policy if cloned under the same computational graph.
- Returns
Newly cloned policy.
- Return type
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network_output_spec(self)¶ Network output spec.
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build(self, *inputs, name=None)¶ Build a Network with the given input(s).
* Do not call tf.global_variable_initializers() after building a model as it will reassign random weights to the model. The parameters inside a model will be initialized when calling build(). *
It uses the same, fixed variable scope for all Networks, to ensure parameter sharing. Different Networks must have an unique name.
- Parameters
- Raises
ValueError – When a Network with the same name is already built.
- Returns
- Output tensors of the model with the given
inputs.
- Return type
list[tf.Tensor]
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network_input_spec(self)¶ Network input spec.
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property
parameters(self)¶ Parameters of the model.
- Returns
Parameters
- Return type
np.ndarray
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property
name(self)¶ Name (str) of the model.
This is also the variable scope of the model.
- Returns
Name of the model.
- Return type
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property
input(self)¶ Default input of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the input of the network.
- Returns
Default input of the model.
- Return type
tf.Tensor
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property
output(self)¶ Default output of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the output of the network.
- Returns
Default output of the model.
- Return type
tf.Tensor
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property
inputs(self)¶ Default inputs of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the inputs of the network.
- Returns
Default inputs of the model.
- Return type
list[tf.Tensor]
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property
outputs(self)¶ Default outputs of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the outputs of the network.
- Returns
Default outputs of the model.
- Return type
list[tf.Tensor]
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reset(self, do_resets=None)¶ Reset the module.
This is effective only to recurrent modules. do_resets is effective only to vectoried modules.
For a vectorized modules, 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
state_info_specs(self)¶ State info specification.
- Returns
- keys and shapes for the information related to the
module’s state when taking an action.
- Return type
List[str]
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property
state_info_keys(self)¶ State info keys.
- Returns
- keys for the information related to the module’s state
when taking an input.
- Return type
List[str]
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terminate(self)¶ Clean up operation.
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get_trainable_vars(self)¶ Get trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
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get_global_vars(self)¶ Get global variables.
- Returns
- A list of global variables in the current
variable scope.
- Return type
List[tf.Variable]
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get_regularizable_vars(self)¶ Get all network weight variables in the current scope.
- Returns
- A list of network weight variables in the
current variable scope.
- Return type
List[tf.Variable]
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get_params(self)¶ Get the trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
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get_param_shapes(self)¶ Get parameter shapes.
- Returns
A list of variable shapes.
- Return type
List[tuple]
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get_param_values(self)¶ Get param values.
- Returns
- Values of the parameters evaluated in
the current session
- Return type
np.ndarray
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set_param_values(self, param_values)¶ Set param values.
- Parameters
param_values (np.ndarray) – A numpy array of parameter values.
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flat_to_params(self, flattened_params)¶ Unflatten tensors according to their respective shapes.
- Parameters
flattened_params (np.ndarray) – A numpy array of flattened params.
- Returns
- A list of parameters reshaped to the
shapes specified.
- Return type
List[np.ndarray]
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property
observation_space(self)¶ Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
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property
action_space(self)¶ Action space.
- Returns
The action space of the environment.
- Return type
akro.Space