garage.tf.policies.gaussian_gru_policy
¶
Gaussian GRU Policy.
A policy represented by a Gaussian distribution which is parameterized by a Gated Recurrent Unit (GRU).
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class
GaussianGRUPolicy
(env_spec, hidden_dim=32, name='GaussianGRUPolicy', 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=None, 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, learn_std=True, std_share_network=False, init_std=1.0, layer_normalization=False, state_include_action=True)¶ Bases:
garage.tf.models.GaussianGRUModel
,garage.tf.policies.policy.Policy
Gaussian GRU Policy.
A policy represented by a Gaussian distribution which is parameterized by a Gated Recurrent Unit (GRU).
Parameters: - env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
- name (str) – Model name, also the variable scope.
- hidden_dim (int) – Hidden dimension for GRU cell for mean.
- 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.
- learn_std (bool) – Is std trainable.
- std_share_network (bool) – Boolean for whether mean and std share the same network.
- init_std (float) – Initial value for std.
- layer_normalization (bool) – Bool for using layer normalization or not.
- state_include_action (bool) – Whether the state includes action. If True, input dimension will be (observation dimension + action dimension).
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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]
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env_spec
¶ Policy environment specification.
Returns: Environment specification. Return type: garage.EnvSpec
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parameters
¶ Parameters of the model.
Returns: Parameters Return type: np.ndarray
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name
¶ Name (str) of the model.
This is also the variable scope of the model.
Returns: Name of the model. Return type: str
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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
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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
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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]
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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]
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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]
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observation_space
¶ Observation space.
Returns: The observation space of the environment. Return type: akro.Space
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action_space
¶ Action space.
Returns: The action space of the environment. Return type: akro.Space
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build
(self, 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 means, with shape \((N, S^*)\). tf.Tensor: Step log std, with shape \((N, S^*)\). tf.Tensor: Step hidden state, with shape \((N, S^*)\). tf.Tensor: Initial hidden state, with shape \((S^*)\).
Return type: tfp.distributions.MultivariateNormalDiag
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reset
(self, 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 parallel environments for training data sampling = 1.
Parameters: do_resets (numpy.ndarray) – Bool that indicates terminal state(s).
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get_action
(self, observation)¶ Get single action from this policy for the input observation.
Parameters: observation (numpy.ndarray) – Observation from environment. Returns: Actions dict: Predicted action and agent information. Return type: numpy.ndarray Note
It returns an action and a dict, with keys - mean (numpy.ndarray): Mean of the distribution. - log_std (numpy.ndarray): Log standard deviation of the
distribution.- prev_action (numpy.ndarray): Previous action, only present if
- self._state_include_action is True.
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get_actions
(self, observations)¶ Get multiple actions from this policy for the input observations.
Parameters: observations (numpy.ndarray) – Observations from environment. Returns: Actions dict: Predicted action and agent information. Return type: numpy.ndarray Note
It returns an action and a dict, with keys - mean (numpy.ndarray): Means of the distribution. - log_std (numpy.ndarray): Log standard deviations of the
distribution.- prev_action (numpy.ndarray): Previous action, only present if
- self._state_include_action is True.
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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: garage.tf.policies.GaussianGRUPolicy
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network_input_spec
(self)¶ Network input spec.
Returns: Name of the model inputs, in order. Return type: list[str]
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network_output_spec
(self)¶ Network output spec.
Returns: Name of the model outputs, in order. 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]