`garage.tf.baselines`¶

Baseline estimators for TensorFlow-based algorithms.

class ContinuousMLPBaseline(env_spec, num_seq_inputs=1, name='ContinuousMLPBaseline', 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(), optimizer=None, optimizer_args=None, normalize_inputs=True)¶

Bases: garage.tf.models.NormalizedInputMLPModel, garage.np.baselines.Baseline

Inheritance diagram of garage.tf.baselines.ContinuousMLPBaseline

A value function using a MLP network.

It fits the input data by performing linear regression to the outputs.

Parameters:

env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
num_seq_inputs (float) – Number of sequence per input. By default it is 1.0, which means only one single sequence.
name (str) – Name of baseline.
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.
optimizer (garage.tf.Optimizer) – Optimizer for minimizing the negative log-likelihood.
optimizer_args (dict) – Arguments for the optimizer. Default is None, which means no arguments.
normalize_inputs (bool) – Bool for normalizing inputs or not.

recurrent¶

If this module has a hidden state.

Type:	bool

env_spec¶

Policy environment specification.

Returns:	Environment specification.
Return type:	garage.EnvSpec

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]

fit(self, paths)¶

Fit regressor based on paths.

Parameters:	paths (dict[numpy.ndarray]) – Sample paths.

predict(self, paths)¶

Predict value based on paths.

Parameters:	paths (dict[numpy.ndarray]) – Sample paths.
Returns:	Predicted value.
Return type:	numpy.ndarray

network_output_spec(self)¶

Network output spec.

Returns:	List of key(str) for the network outputs.
Return type:	list[str]

build(self, *inputs, name=None)¶

Build a Network with the given input(s).

* Do not call tf.global_variable_initializers() after building a model as it will reassign random weights to the model. The parameters inside a model will be initialized when calling build(). *

It uses the same, fixed variable scope for all Networks, to ensure parameter sharing. Different Networks must have an unique name.

Parameters:	inputs (list[tf.Tensor]) – Tensor input(s), recommended to be positional arguments, for example, def build(self, state_input, action_input, name=None). name (str) – Name of the model, which is also the name scope of the model.
Raises:	`ValueError` – When a Network with the same name is already built.
Returns:	Output tensors of the model with the given inputs.
Return type:	list[tf.Tensor]

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_trainable_vars(self)¶

Get trainable variables.

Returns:	A list of trainable variables in the current variable scope.
Return type:	List[tf.Variable]

get_global_vars(self)¶

Get global variables.

Returns:	A list of global variables in the current variable scope.
Return type:	List[tf.Variable]

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]

class GaussianCNNBaseline(env_spec, filters, strides, padding, hidden_sizes, 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(), name='GaussianCNNBaseline', learn_std=True, init_std=1.0, adaptive_std=False, std_share_network=False, std_filters=(), std_strides=(), std_padding='SAME', std_hidden_sizes=(), std_hidden_nonlinearity=None, std_output_nonlinearity=None, layer_normalization=False, normalize_inputs=True, normalize_outputs=True, subsample_factor=1.0, optimizer=None, optimizer_args=None, use_trust_region=True, max_kl_step=0.01)¶

Bases: garage.tf.baselines.gaussian_cnn_baseline_model.GaussianCNNBaselineModel, garage.np.baselines.baseline.Baseline

Inheritance diagram of garage.tf.baselines.GaussianCNNBaseline

Fits a Gaussian distribution to the outputs of a CNN.

Parameters:

env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
filters (Tuple[Tuple[int, Tuple[int, int]], ..]) – Number and dimension of filters. For example, ((3, (3, 5)), (32, (3, 3))) means there are two convolutional layers. The filter for the first layer have 3 channels and its shape is (3 x 5), while the filter for the second layer have 32 channels and its shape is (3 x 3).
strides (tuple[int]) – The stride of the sliding window. For example, (1, 2) means there are two convolutional layers. The stride of the filter for first layer is 1 and that of the second layer is 2.
padding (str) – The type of padding algorithm to use, either ‘SAME’ or ‘VALID’.
name (str) – Model name, also the variable scope.
hidden_sizes (list[int]) – Output dimension of dense layer(s) for the Convolutional model for mean. For example, (32, 32) means the network consists of two dense 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.
name – Name of this model (also used as its scope).
learn_std (bool) – Whether to train the standard deviation parameter of the Gaussian distribution.
init_std (float) – Initial standard deviation for the Gaussian distribution.
adaptive_std (bool) – Whether to use a neural network to learn the standard deviation of the Gaussian distribution. Unless True, the standard deviation is learned as a parameter which is not conditioned on the inputs.
std_share_network (bool) – Boolean for whether the mean and standard deviation models share a CNN network. If True, each is a head from a single body network. Otherwise, the parameters are estimated using the outputs of two indepedent networks.
std_filters (Tuple[Tuple[int, Tuple[int, int]], ..]) – Number and dimension of filters. For example, ((3, (3, 5)), (32, (3, 3))) means there are two convolutional layers. The filter for the first layer have 3 channels and its shape is (3 x 5), while the filter for the second layer have 32 channels and its shape is (3 x 3).
std_strides (tuple[int]) – The stride of the sliding window. For example, (1, 2) means there are two convolutional layers. The stride of the filter for first layer is 1 and that of the second layer is 2.
std_padding (str) – The type of padding algorithm to use in std network, either ‘SAME’ or ‘VALID’.
std_hidden_sizes (list[int]) – Output dimension of dense layer(s) for the Conv for std. For example, (32, 32) means the Conv consists of two hidden layers, each with 32 hidden units.
std_hidden_nonlinearity (callable) – Nonlinearity for each hidden layer in the std network.
std_output_nonlinearity (Callable) – Activation function for output dense layer in the std network. It should return a tf.Tensor. Set it to None to maintain a linear activation.
layer_normalization (bool) – Bool for using layer normalization or not.
normalize_inputs (bool) – Bool for normalizing inputs or not.
normalize_outputs (bool) – Bool for normalizing outputs or not.
subsample_factor (float) – The factor to subsample the data. By default it is 1.0, which means using all the data.
optimizer (garage.tf.Optimizer) – Optimizer used for fitting the model.
optimizer_args (dict) – Arguments for the optimizer. Default is None, which means no arguments.
use_trust_region (bool) – Whether to use a KL-divergence constraint.
max_kl_step (float) – KL divergence constraint for each iteration, if use_trust_region is active.

recurrent¶

If this module has a hidden state.

Type:	bool

env_spec¶

Policy environment specification.

Returns:	Environment specification.
Return type:	garage.EnvSpec

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]

fit(self, paths)¶

Fit regressor based on paths.

Parameters:	paths (dict[numpy.ndarray]) – Sample paths.

predict(self, paths)¶

Predict ys based on input xs.

Parameters:	paths (dict[numpy.ndarray]) – Sample paths.
Returns:	The predicted ys.
Return type:	numpy.ndarray

clone_model(self, name)¶

Return a clone of the GaussianCNNBaselineModel.

It copies the configuration of the primitive and also the parameters.

Parameters:	name (str) – Name of the newly created model. It has to be different from source policy if cloned under the same computational graph.
Returns:	Newly cloned model.
Return type:	garage.tf.baselines.GaussianCNNBaselineModel

network_output_spec(self)¶

Network output spec.

Returns:	List of key(str) for the network outputs.
Return type:	list[str]

build(self, *inputs, name=None)¶

Build a Network with the given input(s).

* Do not call tf.global_variable_initializers() after building a model as it will reassign random weights to the model. The parameters inside a model will be initialized when calling build(). *

It uses the same, fixed variable scope for all Networks, to ensure parameter sharing. Different Networks must have an unique name.

Parameters:	inputs (list[tf.Tensor]) – Tensor input(s), recommended to be positional arguments, for example, def build(self, state_input, action_input, name=None). name (str) – Name of the model, which is also the name scope of the model.
Raises:	`ValueError` – When a Network with the same name is already built.
Returns:	Output tensors of the model with the given inputs.
Return type:	list[tf.Tensor]

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_trainable_vars(self)¶

Get trainable variables.

Returns:	A list of trainable variables in the current variable scope.
Return type:	List[tf.Variable]

get_global_vars(self)¶

Get global variables.

Returns:	A list of global variables in the current variable scope.
Return type:	List[tf.Variable]

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]

class GaussianMLPBaseline(env_spec, num_seq_inputs=1, name='GaussianMLPBaseline', 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(), optimizer=None, optimizer_args=None, use_trust_region=True, max_kl_step=0.01, learn_std=True, init_std=1.0, adaptive_std=False, std_share_network=False, std_hidden_sizes=(32, 32), std_nonlinearity=None, layer_normalization=False, normalize_inputs=True, normalize_outputs=True, subsample_factor=1.0)¶

Bases: garage.tf.baselines.gaussian_mlp_baseline_model.GaussianMLPBaselineModel, garage.np.baselines.Baseline

Inheritance diagram of garage.tf.baselines.GaussianMLPBaseline

Gaussian MLP Baseline with Model.

It fits the input data to a gaussian distribution estimated by a MLP.

Parameters:

env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
subsample_factor (float) – The factor to subsample the data. By default it is 1.0, which means using all the data.
num_seq_inputs (float) – Number of sequence per input. By default it is 1.0, which means only one single sequence.
name (str) – Name of baseline.
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.
optimizer (garage.tf.Optimizer) – Optimizer for minimizing the negative log-likelihood.
optimizer_args (dict) – Arguments for the optimizer. Default is None, which means no arguments.
use_trust_region (bool) – Whether to use trust region constraint.
max_kl_step (float) – KL divergence constraint for each iteration.
learn_std (bool) – Is std trainable.
init_std (float) – Initial value for std.
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.
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.
std_nonlinearity (Callable) – Nonlinearity for each hidden layer in the std network.
layer_normalization (bool) – Bool for using layer normalization or not.
normalize_inputs (bool) – Bool for normalizing inputs or not.
normalize_outputs (bool) – Bool for normalizing outputs or not.
subsample_factor – The factor to subsample the data. By default it is 1.0, which means using all the data.

recurrent¶

If this module has a hidden state.

Type:	bool

env_spec¶

Policy environment specification.

Returns:	Environment specification.
Return type:	garage.EnvSpec

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]

fit(self, paths)¶

Fit regressor based on paths.

Parameters:	paths (list[dict]) – Sample paths.

predict(self, paths)¶

Predict value based on paths.

Parameters:	paths (list[dict]) – Sample paths.
Returns:	Predicted value.
Return type:	numpy.ndarray

clone_model(self, name)¶

Return a clone of the GaussianMLPBaselineModel.

It copies the configuration of the primitive and also the parameters.

Parameters:	name (str) – Name of the newly created model. It has to be different from source policy if cloned under the same computational graph.
Returns:	Newly cloned model.
Return type:	garage.tf.baselines.GaussianMLPBaselineModel

network_output_spec(self)¶

Network output spec.

Returns:	List of key(str) for the network outputs.
Return type:	list[str]

build(self, *inputs, name=None)¶

Build a Network with the given input(s).

* Do not call tf.global_variable_initializers() after building a model as it will reassign random weights to the model. The parameters inside a model will be initialized when calling build(). *

It uses the same, fixed variable scope for all Networks, to ensure parameter sharing. Different Networks must have an unique name.

Parameters:	inputs (list[tf.Tensor]) – Tensor input(s), recommended to be positional arguments, for example, def build(self, state_input, action_input, name=None). name (str) – Name of the model, which is also the name scope of the model.
Raises:	`ValueError` – When a Network with the same name is already built.
Returns:	Output tensors of the model with the given inputs.
Return type:	list[tf.Tensor]

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_trainable_vars(self)¶

Get trainable variables.

Returns:	A list of trainable variables in the current variable scope.
Return type:	List[tf.Variable]

get_global_vars(self)¶

Get global variables.

Returns:	A list of global variables in the current variable scope.
Return type:	List[tf.Variable]

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]

garage.tf.baselines¶

`garage.tf.baselines`¶