bolero.optimizer.ContextualOptimizer¶

class bolero.optimizer.ContextualOptimizer[source]¶

Common interface for (contextual) optimizers.

This is a simple derivative-free parameter optimizer.

__init__()¶

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

best_policy()[source]¶

Return current best estimate of contextual policy.

Returns:	policy : UpperLevelPolicy Best estimate of upper-level policy

get_args()¶

Get parameters for this estimator.

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

get_desired_context()[source]¶

Chooses desired context for next evaluation.

Returns:	context : ndarray-like, default=None The context in which the next rollout shall be performed. If None, the environment may select the next context without any preferences.

get_next_parameters(params)[source]¶

Get next individual/parameter vector for evaluation.

Parameters:	params : array_like, shape (n_params,) Parameter vector, will be modified

init(n_params, n_context_dims)[source]¶

Initialize optimizer.

Parameters:	n_params : int dimension of the parameter vector n_context_dims : int number of dimensions of the context space

is_behavior_learning_done()[source]¶

Check if the optimization is finished.

Returns:	finished : bool Is the learning of a behavior finished?

set_context(context)[source]¶

Set context of next evaluation.

Note that the set context need not necessarily be the same that was requested by get_desired_context().

Parameters:	context : array-like, shape (n_context_dims,) The context in which the next rollout will be performed

set_evaluation_feedback(rewards)[source]¶

Set feedbacks for the parameter vector.

Parameters:	rewards : list of float Feedbacks for each step or for the episode, depends on the problem