I have built my own RL environment, where a state is composed of two elements: the agent's position and a matrix of 0s and 1s (1 if a user has requested a service from the agent, 0 otherwise); an action is composed of 3 elements: the movement the agent chooses (up, down, left or right), a matrix of 0s and 1s (1 if a resource has been allocated to a user, 0 otherwise), and a vector representing the allocation of another type of resource (the vector contains the values allocated to the users).
I am currently trying to build a Deep Q Learning agent, I am a bit confused however as to what model (example Sequential), what type of layers (example Dense layers), how many layers, what activation mode I should use, and what the state and action sizes are. (Taking this code as a reference cartpole dqn agent)
I also do not know what my inputs and outputs should be.
The examples I have come across are rather simple and I don't know how to approach setting it all up for my agent.