# garage.tf.policies.gaussian_mlp_task_embedding_policy¶

class GaussianMLPTaskEmbeddingPolicy(env_spec, encoder, name='GaussianMLPTaskEmbeddingPolicy', hidden_sizes=(32, 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(), output_nonlinearity=None, output_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_b_init=tf.zeros_initializer(), learn_std=True, adaptive_std=False, std_share_network=False, init_std=1.0, min_std=1e-06, max_std=None, std_hidden_sizes=(32, 32), std_hidden_nonlinearity=tf.nn.tanh, std_output_nonlinearity=None, std_parameterization='exp', layer_normalization=False)

Parameters
• env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.

• encoder (garage.tf.embeddings.StochasticEncoder) – Embedding network.

• name (str) – Model name, also the variable scope.

• 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.

• 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.

• learn_std (bool) – Is std trainable.

• adaptive_std (bool) – Is std a neural network. If False, it will be a parameter.

• std_share_network (bool) – Boolean for whether mean and std share the same network.

• init_std (float) – Initial value for std.

• std_hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for std. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.

• min_std (float) – If not None, the std is at least the value of min_std, to avoid numerical issues.

• max_std (float) – If not None, the std is at most the value of max_std, to avoid numerical issues.

• std_hidden_nonlinearity (callable) – Nonlinearity for each hidden layer in the std network. It should return a tf.Tensor. Set it to None to maintain a linear activation.

• std_output_nonlinearity (callable) – Nonlinearity for output layer in the std network. It should return a tf.Tensor. Set it to None to maintain a linear activation.

• std_parameterization (str) –

How the std should be parametrized. There are a few options: - exp: the logarithm of the std will be stored, and applied a

exponential transformation

• softplus: the std will be computed as log(1+exp(x))

• layer_normalization (bool) – Bool for using layer normalization or not.

build(self, obs_input, task_input, name=None)

Build policy.

Parameters
• obs_input (tf.Tensor) – Observation input.

• name (str) – Name of the model, which is also the name scope.

Returns

Policy network. namedtuple: Encoder network.

Return type

namedtuple

get_action(self, observation)

Get action sampled from the policy.

Parameters

observation (np.ndarray) – Augmented observation from the environment, with shape $$(O+N, )$$. O is the dimension of observation, N is the number of tasks.

Returns

Action sampled from the policy,

with shape $$(A, )$$. A is the dimension of action.

dict: Action distribution information, with keys:
• mean (numpy.ndarray): Mean of the distribution,

with shape $$(A, )$$. A is the dimension of action.

• log_std (numpy.ndarray): Log standard deviation of the

distribution, with shape $$(A, )$$. A is the dimension of action.

Return type

np.ndarray

get_actions(self, observations)

Get actions sampled from the policy.

Parameters

observations (np.ndarray) – Augmented observation from the environment, with shape $$(T, O+N)$$. T is the number of environment steps, O is the dimension of observation, N is the number of tasks.

Returns

Actions sampled from the policy,

with shape $$(T, A)$$. T is the number of environment steps, A is the dimension of action.

dict: Action distribution information, with keys:
• mean (numpy.ndarray): Mean of the distribution,

with shape $$(T, A)$$. T is the number of environment steps, A is the dimension of action.

• log_std (numpy.ndarray): Log standard deviation of the

distribution, with shape $$(T, A)$$. T is the number of environment steps, Z is the dimension of action.

Return type

np.ndarray

get_action_given_latent(self, observation, latent)

Sample an action given observation and latent.

Parameters
• observation (np.ndarray) – Observation from the environment, with shape $$(O, )$$. O is the dimension of observation.

• latent (np.ndarray) – Latent, with shape $$(Z, )$$. Z is the dimension of the latent embedding.

Returns

Action sampled from the policy,

with shape $$(A, )$$. A is the dimension of action.

dict: Action distribution information, with keys:
• mean (numpy.ndarray): Mean of the distribution,

with shape $$(A, )$$. A is the dimension of action.

• log_std (numpy.ndarray): Log standard deviation of the

distribution, with shape $$(A, )$$. A is the dimension of action.

Return type

np.ndarray

get_actions_given_latents(self, observations, latents)

Sample a batch of actions given observations and latents.

Parameters
• observations (np.ndarray) – Observations from the environment, with shape $$(T, O)$$. T is the number of environment steps, O is the dimension of observation.

• latents (np.ndarray) – Latents, with shape $$(T, Z)$$. T is the number of environment steps, Z is the dimension of latent embedding.

Returns

Actions sampled from the policy,

with shape $$(T, A)$$. T is the number of environment steps, A is the dimension of action.

dict: Action distribution information, , with keys:
• mean (numpy.ndarray): Mean of the distribution,

with shape $$(T, A)$$. T is the number of environment steps. A is the dimension of action.

• log_std (numpy.ndarray): Log standard deviation of the

distribution, with shape $$(T, A)$$. T is the number of environment steps. A is the dimension of action.

Return type

np.ndarray

get_action_given_task(self, observation, task_id)

Sample an action given observation and task id.

Parameters
• observation (np.ndarray) – Observation from the environment, with shape $$(O, )$$. O is the dimension of the observation.

• task_id (np.ndarray) – One-hot task id, with shape :math:(N, ). N is the number of tasks.

Returns

Action sampled from the policy, with shape

$$(A, )$$. A is the dimension of action.

dict: Action distribution information, with keys:
• mean (numpy.ndarray): Mean of the distribution,

with shape $$(A, )$$. A is the dimension of action.

• log_std (numpy.ndarray): Log standard deviation of the

distribution, with shape $$(A, )$$. A is the dimension of action.

Return type

np.ndarray

get_actions_given_tasks(self, observations, task_ids)

Sample a batch of actions given observations and task ids.

Parameters
• observations (np.ndarray) – Observations from the environment, with shape $$(T, O)$$. T is the number of environment steps, O is the dimension of observation.

• task_ids (np.ndarry) – One-hot task ids, with shape $$(T, N)$$. T is the number of environment steps, N is the number of tasks.

Returns

Actions sampled from the policy,

with shape $$(T, A)$$. T is the number of environment steps, A is the dimension of action.

dict: Action distribution information, , with keys:
• mean (numpy.ndarray): Mean of the distribution,

with shape $$(T, A)$$. T is the number of environment steps. A is the dimension of action.

• log_std (numpy.ndarray): Log standard deviation of the

distribution, with shape $$(T, A)$$. T is the number of environment steps. A is the dimension of action.

Return type

np.ndarray

get_trainable_vars(self)

Get trainable variables.

The trainable vars of a multitask policy should be the trainable vars of its model and the trainable vars of its embedding model.

Returns

A list of trainable variables in the current

variable scope.

Return type

List[tf.Variable]

get_global_vars(self)

Get global variables.

The global vars of a multitask policy should be the global vars of its model and the trainable vars of its embedding model.

Returns

A list of global variables in the current

variable scope.

Return type

List[tf.Variable]

property env_spec(self)

Policy environment specification.

Returns

Environment specification.

Return type

garage.EnvSpec

property encoder(self)

garage.tf.embeddings.encoder.Encoder: Encoder.

property augmented_observation_space(self)

akro.Box: Concatenated observation space and one-hot task id.

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

Cloned policy.

Return type

network_output_spec(self)

Network output spec.

Returns

List of key(str) for the network outputs.

Return type

list[str]

network_input_spec(self)

Network input spec.

Returns

List of key(str) for the network inputs.

Return type

list[str]

property parameters(self)

Parameters of the model.

Returns

Parameters

Return type

np.ndarray

property name(self)

Name (str) of the model.

This is also the variable scope of the model.

Returns

Name of the model.

Return type

str

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

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

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]

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]

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.

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]

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]

terminate(self)

Clean up operation.

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]

get_latent(self, task_id)

Get embedded task id in latent space.

Parameters

task_id (np.ndarray) – One-hot task id, with shape $$(N, )$$. N is the number of tasks.

Returns

An embedding sampled from embedding distribution, with

shape $$(Z, )$$. Z is the dimension of the latent embedding.

dict: Embedding distribution information.

Return type

np.ndarray

property latent_space(self)

akro.Box: Space of latent.

property task_space(self)

akro.Box: One-hot space of task id.

property encoder_distribution(self)

tfp.Distribution.MultivariateNormalDiag: Encoder distribution.

split_augmented_observation(self, collated)

Splits up observation into one-hot task and environment observation.

Parameters

collated (np.ndarray) – Environment observation concatenated with task one-hot, with shape $$(O+N, )$$. O is the dimension of observation, N is the number of tasks.

Returns

Vanilla environment observation,

with shape $$(O, )$$. O is the dimension of observation.

np.ndarray: Task one-hot, with shape $$(N, )$$. N is the number

Return type

np.ndarray

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