Most if not all AI systems are not to imitate human, but to finally out-perform human. Examples include using AI to play a game, classification problems, auto-driving, and goal-oriented chatbots. Those tasks usually comes with an easily and clearly defined value function, which is the objective function for the AI to optimize.
My question is, how is DNN reinforcement learning or related techniques to be applied to AI system that are designed to just imitate human but not outperform human? Note this is different from a human-like system. Our objective here is to let the AI become a human rather than a super intelligence. For example, if human consistently makes a mistake in image identification, then the AI system must also make the same mistake. Another example is the classic chatbot to pass the Turing test. Is DNN reinforcement learning useful in these kind of tasks? I find it is really hard to start with because the value function cannot be easily calculated. What are some theory behind this?