garage.tf.baselines.continuous_mlp_baseline

A value function (baseline) based on a MLP model.

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.continuous_mlp_baseline.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]