Suppose that we want to train a car to drive in the real world and decide to use Reinforcement Learning (specifically, DQN) for that. I am a bit confused about how training generally works.
Is it that we are exploring the environment at the same time that we are training the Q network? If so, is there not a way to train the Q network before actually going out into the real world? And then, aren't there millions of possible states in the real world? So, how does RL or I guess the neural network generalize so that it can function during rush hour, empty roads, etc.