I'm aware that we back-propagate after computing the loss between:
The Neural Network Q values and the Target Network Q values
However, all this is doing is updating the parameters of the Neural Network to produce an output that matches the Target Q values as close as possible.
Suppose one epoch is run and the reward is +10, surely we need to update the parameters using this too to tell the Network to push the probability of these actions, given these parameters up.
How does the algorithm know +10 is good? Suppose the reward range is -10 for loss and +10 for win.