In reinforcement learning, there are states, actions, initial states, terminal states, a progress function and a reward function. Is there a theoretical framework or setting where states and actions are from the same domain?
So the goal would be to learn a policy to manipulate an input stream in a way that optimizes a reward function. Input would be a state vector and output (i.e. action) would be another vector of the same domain.