How do I calculate the error during the training phase for deep reinforcement learning models?
Deep reinforcement learning is not supervised learning as far as I know. So how can the model know whether it predicts right or wrong? In literature, I find that the "actual" Q-value is calculated, but that sounds like the whole idea behind deep RL is obsolete. How could I even calculate/know the real Q-value if there is not already a world model existing?