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
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.
- property env_spec¶
Policy environment specification.
- Returns
Environment specification.
- Return type
- property encoder¶
Encoder.
- property augmented_observation_space¶
Concatenated observation space and one-hot task id.
- Type
akro.Box
- property parameters¶
Parameters of the model.
- Returns
Parameters
- Return type
np.ndarray
- property name¶
Name (str) of the model.
This is also the variable scope of the model.
- Returns
Name of the model.
- Return type
- property 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
- property 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
- property 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]
- property 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]
- property 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]
- property 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]
- property latent_space¶
Space of latent.
- Type
akro.Box
- property task_space¶
One-hot space of task id.
- Type
akro.Box
- property encoder_distribution¶
Encoder distribution.
- Type
tfp.Distribution.MultivariateNormalDiag
- property observation_space¶
Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
- property action_space¶
Action space.
- Returns
The action space of the environment.
- Return type
akro.Space
- build(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(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(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(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(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(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(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()¶
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()¶
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(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()¶
Network output spec.
- network_input_spec()¶
Network input spec.
- reset(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()¶
Clean up operation.
- get_regularizable_vars()¶
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()¶
Get the trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_param_shapes()¶
Get parameter shapes.
- Returns
A list of variable shapes.
- Return type
List[tuple]
- get_param_values()¶
Get param values.
- Returns
- Values of the parameters evaluated in
the current session
- Return type
np.ndarray
- set_param_values(param_values)¶
Set param values.
- Parameters
param_values (np.ndarray) – A numpy array of parameter values.
- flat_to_params(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(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(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