bolero.representation.BlackBoxBehavior¶

class bolero.representation.BlackBoxBehavior[source]¶

Can be optimized with black box optimizer.

A behavior that can be optimized with a black box optimizer must be exactly defined by a fixed number of parameters.

Parameters:	n_inputs : int Number of input components. n_outputs : int Number of output components.

__init__()¶

x.__init__(…) initializes x; see help(type(x)) for signature

can_step()¶

Returns if step() can be called again.

Returns:	can_step : bool Can we call step() again?

get_args()¶

Get parameters for this estimator.

Returns:	params : mapping of string to any Parameter names mapped to their values.

get_n_params()[source]¶

Get number of parameters.

Returns:	n_params : int Number of parameters that will be optimized.

get_outputs(outputs)¶

Get outputs of the last step.

If the output vector consists of positions and derivatives of these, by convention all positions and all derivatives should be stored contiguously.

Parameters:	outputs : array-like, shape = (n_outputs,) outputs, e.g. next action, will be updated

get_params()[source]¶

Get current parameters.

Returns:	params : array-like, shape = (n_params,) Current parameters.

init(n_inputs, n_outputs)¶

Initialize the behavior.

Parameters:	n_inputs : int number of inputs n_outputs : int number of outputs

reset()[source]¶

Reset behavior.

This method is usually called after setting the parameters to reuse the current behavior and clear its internal state.

set_inputs(inputs)¶

Set input for the next step.

If the input vector consists of positions and derivatives of these, by convention all positions and all derivatives should be stored contiguously.

Parameters:	inputs : array-like, shape = (n_inputs,) inputs, e.g. current state of the system

set_meta_parameters(keys, meta_parameters)¶

Set meta-parameters.

Meta-parameters could be the goal, obstacles, …

Parameters:	keys : list of string names of meta-parameters meta_parameters : list of lists of float values One list of floats for each parameter

set_params(params)[source]¶

Set new parameter values.

Parameters:	params : array-like, shape = (n_params,) New parameters.

step()¶

Compute output for the received input.

Uses the inputs and meta-parameters to compute the outputs.