I have read through most of the current documentations on the Deep Q-Network in Matlab, but it is still not very clear to me how to construct a Deep Q-Network in my case.
I previously wrote my own code for implementing a simple Q-learning, for which, I constructed a Q-matrix with corresponding states and actions. I am now trying to explore how to do the same with Deep Q-Network.
The overall goal is to trying to work out a best policy for an object to move from location A to location B (assuming it is in 2-D)
I have a specific function that has all the necessary physical relationship which will return the corresponding rewards given the current state and action. (lets' say it is called the function F).
In my case, since I can return the specific rewards per action given current state, what should I put down as my observation? (How should I incorporate my function F into the agent?)
Also, in the documentations, I don't see anywhere it takes rewards or calculate rewards for certain actions.
Could somone help me please?