garage.tf.policies.gaussian_mlp_task_embedding_policy

GaussianMLPTaskEmbeddingPolicy.

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)

Bases: garage.tf.models.GaussianMLPModel, garage.tf.policies.task_embedding_policy.TaskEmbeddingPolicy

Inheritance diagram of garage.tf.policies.gaussian_mlp_task_embedding_policy.GaussianMLPTaskEmbeddingPolicy

GaussianMLPTaskEmbeddingPolicy.

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.
env_spec

Policy environment specification.

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

Encoder.

Type:garage.tf.embeddings.encoder.Encoder
augmented_observation_space

Concatenated observation space and one-hot task id.

Type:akro.Box
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_specs

State info specification.

Returns:
keys and shapes for the information related to the
module’s state when taking an action.
Return type:List[str]
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]
latent_space

Space of latent.

Type:akro.Box
task_space

One-hot space of task id.

Type:akro.Box
encoder_distribution

Encoder distribution.

Type:tfp.Distribution.MultivariateNormalDiag
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, obs_input, task_input, name=None)

Build policy.

Parameters:
  • obs_input (tf.Tensor) – Observation input.
  • task_input (tf.Tensor) – One-hot task id 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]
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:garage.tf.policies.GaussianMLPTaskEmbeddingPolicy
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]
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.
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
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
of tasks.
Return type:np.ndarray