Humans instead learn based on reinforcement, be it positive or negative. So the human essentially builds a world model of what is "good" and "acceptable", and then works against that.
I suspect that a Reinforcement Learning approach to art generation should be producing art that is much better than current works simply because it doesn't aim to imitate, but instead it aims to maximize rewards, or the fitness of the work without relying on individual measurements against an ideal.
It should be noted that RL research is very costly, perhaps a more feasible approach is to train a model to generate art based on noise, and then train the RL agent to generate good noise.
Humans instead learn based on reinforcement, be it positive or negative. So the human essentially builds a world model of what is "good" and "acceptable", and then works against that.
I suspect that a Reinforcement Learning approach to art generation should be producing art that is much better than current works simply because it doesn't aim to imitate, but instead it aims to maximize rewards, or the fitness of the work without relying on individual measurements against an ideal.
It should be noted that RL research is very costly, perhaps a more feasible approach is to train a model to generate art based on noise, and then train the RL agent to generate good noise.