garage.tf.policies.categorical_gru_policy
¶
Categorical GRU Policy.
A policy represented by a Categorical distribution which is parameterized by a Gated Recurrent Unit (GRU).
- class CategoricalGRUPolicy(env_spec, name='CategoricalGRUPolicy', hidden_dim=32, hidden_nonlinearity=tf.nn.tanh, hidden_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), hidden_b_init=tf.zeros_initializer(), recurrent_nonlinearity=tf.nn.sigmoid, recurrent_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_nonlinearity=tf.nn.softmax, output_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_b_init=tf.zeros_initializer(), hidden_state_init=tf.zeros_initializer(), hidden_state_init_trainable=False, state_include_action=True, layer_normalization=False)¶
Bases:
garage.tf.models.CategoricalGRUModel
,garage.tf.policies.policy.Policy
Categorical GRU Policy.
A policy represented by a Categorical distribution which is parameterized by a Gated Recurrent Unit (GRU).
It only works with akro.Discrete action space.
- Parameters
env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
name (str) – Policy name, also the variable scope.
hidden_dim (int) – Hidden dimension for LSTM cell.
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.
recurrent_nonlinearity (callable) – Activation function for recurrent layers. It should return a tf.Tensor. Set it to None to maintain a linear activation.
recurrent_w_init (callable) – Initializer function for the weight of recurrent 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.
hidden_state_init (callable) – Initializer function for the initial hidden state. The functino should return a tf.Tensor.
hidden_state_init_trainable (bool) – Bool for whether the initial hidden state is trainable.
state_include_action (bool) – Whether the state includes action. If True, input dimension will be (observation dimension + action dimension).
layer_normalization (bool) – Bool for using layer normalization or not.
- property env_spec¶
Policy environment specification.
- Returns
Environment specification.
- Return type
- property state_info_specs¶
State info specifcation.
- Returns
- keys and shapes for the information related to the
policy’s state when taking an action.
- Return type
List[str]
- property parameters¶
Parameters of the model.
- Returns
Parameters
- Return type
np.ndarray
- property name¶
Name (str) of the model.
This is also the variable scope of the model.
- Returns
Name of the model.
- Return type
- property input¶
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
- property output¶
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
- property inputs¶
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]
- property outputs¶
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]
- property state_info_keys¶
State info keys.
- Returns
- keys for the information related to the module’s state
when taking an input.
- Return type
List[str]
- property observation_space¶
Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
- property action_space¶
Action space.
- Returns
The action space of the environment.
- Return type
akro.Space
- build(state_input, name=None)¶
Build policy.
- Parameters
state_input (tf.Tensor) – State input.
name (str) – Name of the policy, which is also the name scope.
- Returns
Policy distribution. tf.Tensor: Step output, with shape \((N, S^*)\). tf.Tensor: Step hidden state, with shape \((N, S^*)\). tf.Tensor: Initial hidden state , used to reset the hidden state
when policy resets. Shape: \((S^*)\).
- Return type
tfp.distributions.OneHotCategorical
- reset(do_resets=None)¶
Reset the policy.
Note
If do_resets is None, it will be by default np.array([True]), which implies the policy will not be “vectorized”, i.e. number of paralle environments for training data sampling = 1.
- Parameters
do_resets (numpy.ndarray) – Bool that indicates terminal state(s).
- get_action(observation)¶
Return a single action.
- Parameters
observation (numpy.ndarray) – Observations.
- Returns
Action given input observation. dict(numpy.ndarray): Distribution parameters.
- Return type
- get_actions(observations)¶
Return multiple actions.
- clone(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
- network_output_spec()¶
Network output spec.
- network_input_spec()¶
Network input spec.
- terminate()¶
Clean up operation.
- get_trainable_vars()¶
Get trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_global_vars()¶
Get global variables.
- Returns
- A list of global variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_regularizable_vars()¶
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]
- get_params()¶
Get the trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_param_shapes()¶
Get parameter shapes.
- Returns
A list of variable shapes.
- Return type
List[tuple]
- get_param_values()¶
Get param values.
- Returns
- Values of the parameters evaluated in
the current session
- Return type
np.ndarray
- set_param_values(param_values)¶
Set param values.
- Parameters
param_values (np.ndarray) – A numpy array of parameter values.
- flat_to_params(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]