There's many types of Machine Learning methods, but one sticks in my mind, and I don't understand why for ex. Q Learning was made instead of just using the following method:Method:Say we give the robot some Triggers, we will implant a pre-installed image of a girl, taste of fries, sound of birds, and acceleration. When it Guesses Actions - that get a trigger, it saves actions just done and links them to senses just saved and labels the senses&actions as + or - based on if matched a + or - trigger and gives it a rating of how much it matched, then when it senses it again later, it matches memory and links to the actions and they are initiated, it walks to the girl/sound of birds chirping. Seeing that, why is Q Learning so complex the way it works?
Doesn't this method of Machine Learning do perfectly fine?
machine learning