garage.tf.policies.task_embedding_policy module

Policy class for Task Embedding envs.

class TaskEmbeddingPolicy(name, env_spec, encoder)

Bases: garage.tf.policies.policy.StochasticPolicy

Base class for Task Embedding policies in TensorFlow.

This policy needs a task id in addition to observation to sample an action.

Parameters:

• name (str) – Policy name, also the variable scope.
• env_spec (garage.envs.EnvSpec) – Environment specification.
• encoder (garage.tf.embeddings.StochasticEncoder) – A encoder that embeds a task id to a latent.

augmented_observation_space

Concatenated observation space and one-hot task id.

Type:akro.Box

encoder

Encoder.

Type:garage.tf.embeddings.encoder.Encoder

encoder_distribution

Encoder distribution.

Type:garage.tf.distributions.DiagonalGaussian

get_action(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.

Return type:np.ndarray

get_action_given_latent(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 latent embedding.

Returns:

Action sampled from the policy,

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

dict: Action distribution information.

Return type:

np.ndarray

get_action_given_task(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.

Return type:

np.ndarray

get_actions(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.

Return type:np.ndarray

get_actions_given_latents(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.

Return type:

np.ndarray

get_actions_given_tasks(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.

Return type:

np.ndarray

get_global_vars()

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]

get_latent(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

get_trainable_vars()

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]

latent_space

Space of latent.

Type:akro.Box

split_augmented_observation(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

task_space

One-hot space of task id.

Type:akro.Box