garage.tf.policies.gaussian_lstm_policy

Gaussian LSTM Policy.

A policy represented by a Gaussian distribution which is parameterized by a Long short-term memory (LSTM).

class GaussianLSTMPolicy(env_spec, hidden_dim=32, name='GaussianLSTMPolicy', 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, cell_state_init=tf.zeros_initializer(), cell_state_init_trainable=False, forget_bias=True, learn_std=True, std_share_network=False, init_std=1.0, layer_normalization=False, state_include_action=True)

Bases: garage.tf.models.GaussianLSTMModel, garage.tf.policies.policy.Policy

Inheritance diagram of garage.tf.policies.gaussian_lstm_policy.GaussianLSTMPolicy

Gaussian LSTM Policy.

A policy represented by a Gaussian distribution which is parameterized by a Long short-term memory (LSTM).

Parameters:
  • env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
  • name (str) – Model name, also the variable scope.
  • hidden_dim (int) – Hidden dimension for LSTM 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.
  • cell_state_init (Callable) – Initializer function for the initial cell state. The functino should return a tf.Tensor.
  • cell_state_init_trainable (bool) – Bool for whether the initial cell state is trainable.
  • forget_bias (bool) – If True, add 1 to the bias of the forget gate at initialization. It’s used to reduce the scale of forgetting at the beginning of the training.
  • 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).
input_dim

Dimension of the policy input.

Type:int
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]
env_spec

Policy environment specification.

Returns:Environment specification.
Return type:garage.EnvSpec
parameters

Parameters of the model.

Returns:Parameters
Return type:np.ndarray
name

Name (str) of the model.

This is also the variable scope of the model.

Returns:Name of the model.
Return type:str
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
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
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]
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]
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]
observation_space

Observation space.

Returns:The observation space of the environment.
Return type:akro.Space
action_space

Action space.

Returns:The action space of the environment.
Return type:akro.Space
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: Step cell state, with shape \((N, S^*)\). tf.Tensor: Initial hidden state, with shape \((S^*)\). tf.Tensor: Initial cell state, with shape \((S^*)\)
Return type:

tfp.distributions.MultivariateNormalDiag
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 paralle environments for training data sampling = 1.

Parameters:do_resets (numpy.ndarray) – Bool that indicates terminal state(s).
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.
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.
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.GaussianLSTMPolicy
network_input_spec(self)

Network input spec.

Returns:Name of the model inputs, in order.
Return type:list[str]
network_output_spec(self)

Network output spec.

Returns:Name of the model outputs, in order.
Return type:list[str]
terminate(self)

Clean up operation.

get_trainable_vars(self)

Get trainable variables.

Returns:
A list of trainable variables in the current
variable scope.
Return type:List[tf.Variable]
get_global_vars(self)

Get global variables.

Returns:
A list of global variables in the current
variable scope.
Return type:List[tf.Variable]
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]
get_params(self)

Get the trainable variables.

Returns:
A list of trainable variables in the current
variable scope.
Return type:List[tf.Variable]
get_param_shapes(self)

Get parameter shapes.

Returns:A list of variable shapes.
Return type:List[tuple]
get_param_values(self)

Get param values.

Returns:
Values of the parameters evaluated in
the current session
Return type:np.ndarray
set_param_values(self, param_values)

Set param values.

Parameters:param_values (np.ndarray) – A numpy array of parameter values.
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]