# Is there a framework for reinforcement learning with states and actions in the same domain?

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.

• A policy in reinforcement learning is a function mapping states to each other. It's a sort of instruction manual for what to do from each state, rather than a vector. It sounds like you're trying to choose the input that optimizes an objective function, is that right? Feb 19 '16 at 21:45

This is the case when for example you have $\mathcal{S} \triangleq \mathbb{R}^d, \mathcal{A} \triangleq \mathbb{R}^d$ like 2d navigation with $d = 2$ (states are locations, actions are vectors indicating direction). Doing RL in that case does not change and there is not special theory either.