def getAction(self):
""" activates the module with the last observation and stores the result as last action. """
# get greedy action
action = LearningAgent.getAction(self)
# explore by chance
if random.random() < self.epsilon:
action = array([random.randint(self.module.numActions)])
# reduce epsilon
self.epsilon *= self.epsilondecay
return action
