from causal_world.intervention_actors.base_actor import \
BaseInterventionActorPolicy
import numpy as np
[docs]class VisualInterventionActorPolicy(BaseInterventionActorPolicy):
[docs] def __init__(self, **kwargs):
"""
This intervention actor intervenes on all visual components of the
robot, (i.e: colors).
:param kwargs:
"""
super(VisualInterventionActorPolicy, self).__init__()
self.task_intervention_space = None
[docs] def initialize(self, env):
"""
This functions allows the intervention actor to query things from the env, such
as intervention spaces or to have access to sampling funcs for goals..etc
:param env: (causal_world.env.CausalWorld) the environment used for the
intervention actor to query
different methods from it.
:return:
"""
self.task_intervention_space = env.get_variable_space_used()
return
def _act(self, variables_dict):
"""
:param variables_dict:
:return:
"""
interventions_dict = dict()
for variable in self.task_intervention_space:
if isinstance(self.task_intervention_space[variable], dict):
if 'color' in self.task_intervention_space[variable]:
interventions_dict[variable] = dict()
interventions_dict[variable]['color'] = np.random.uniform(
self.task_intervention_space[variable]['color'][0],
self.task_intervention_space[variable]['color'][1])
elif 'color' in variable:
interventions_dict[variable] = np.random.uniform(
self.task_intervention_space[variable][0],
self.task_intervention_space[variable][1])
return interventions_dict
[docs] def get_params(self):
"""
returns parameters that could be used in recreating this intervention
actor.
:return: (dict) specifying paramters to create this intervention actor
again.
"""
return {'visual_actor': dict()}