Most, if not all, AI systems do not imitate humans. Some of them out-perform humans. Examples include using AI to play a game, classification problems, auto-driving, and goal-oriented chatbots. Those tasks usually come with an easily and clearly defined value function, which is the objective function for the AI to optimize.
My question is: how is deep reinforcement learning, or related techniques, to be applied to an AI system that is designed to just imitate humans but not outperform humans?
Note this is different from a human-like system. Our objective here is to let the AI become a human rather than a superintelligence. For example, if a 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 deep reinforcement learning useful in these kinds of tasks?
I find it is really hard to start with because the value function cannot be easily calculated.
What is some theory behind this?