garage.tf.policies.gaussian_mlp_task_embedding_policy module¶

GaussianMLPTaskEmbeddingPolicy.

class GaussianMLPTaskEmbeddingPolicy(env_spec, encoder, name='GaussianMLPTaskEmbeddingPolicy', hidden_sizes=(32, 32), hidden_nonlinearity=<function tanh>, hidden_w_init=<tensorflow.python.ops.init_ops_v2.GlorotUniform object>, hidden_b_init=<tensorflow.python.ops.init_ops_v2.Zeros object>, output_nonlinearity=None, output_w_init=<tensorflow.python.ops.init_ops_v2.GlorotUniform object>, output_b_init=<tensorflow.python.ops.init_ops_v2.Zeros object>, 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=<function tanh>, std_output_nonlinearity=None, std_parameterization='exp', layer_normalization=False)[source]¶

Bases: garage.tf.policies.task_embedding_policy.TaskEmbeddingPolicy

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.

build(obs_input, task_input, name=None)[source]¶

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

clone(name)[source]¶

Return a clone of the policy.

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

distribution¶

Policy action distribution.

Returns:	Policy distribution.
Return type:	tfp.Distribution.MultivariateNormalDiag

get_action(observation)[source]¶

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_action_given_latent(observation, latent)[source]¶

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_action_given_task(observation, task_id)[source]¶

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(observations)[source]¶

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_actions_given_latents(observations, latents)[source]¶

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_actions_given_tasks(observations, task_ids)[source]¶

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