bolero.environment.Environment¶

class bolero.environment.Environment[source]¶

Common interface for environments.

An environment can execute actions, measure states and compute rewards. It defines a learning problem.

__init__()¶

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

get_args()¶

Get parameters for this estimator.

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

get_feedback()[source]¶

Get the feedbacks for the last evaluation period.

Returns:	feedbacks : array Feedback values

get_maximum_feedback()[source]¶

Returns the maximum sum of feedbacks obtainable.

get_num_inputs()[source]¶

Get number of environment inputs.

Parameters:	n_inputs : int Number of environment inputs

get_num_outputs()[source]¶

Get number of environment outputs.

Parameters:	n_outputs : int Number of environment outputs

get_outputs(values)[source]¶

Get environment outputs, e.g. state of the environment.

Parameters:	values : array Outputs of the environment, will be modified

init()[source]¶

Initialize environment.

is_behavior_learning_done()[source]¶

Check if the behavior learning is finished.

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

is_evaluation_done()[source]¶

Check if the evaluation of the behavior is finished.

Returns:	finished : bool Is the evaluation finished?

reset()[source]¶

Reset state of the environment.

set_inputs(values)[source]¶

Set environment inputs, e.g. next action.

Parameters:	values : array, Input of the environment

step_action()[source]¶

Take a step in the environment.