I'm doing a Deep Q-learning project. All of my rewards are positive and there are two terminal states. One of them has a zero reward and the other has a high positive reward.
The rewards are stochastic and my Q-network must generate non-zero Q-values for all states and actions. Based on my project, I must use these numbers to create a probability density. In other words, the normalized Q-values of each state generated by network define a probability density for choosing an action.
How should I define my loss function? Is there a project or paper which I could look at and decide how to define the loss function? I am searching for similar projects and their proposed Deep Q-learning algorithms.