# How can reinforcement learning be applied when the goal location or environment is unknown?

I am studying RL. I was thinking whether a new state value or the observation is provided by the environment before the agent actually implements the action.

Take the maze problem as an example. Each state consists of all the available cells information, provided by the environment. But what if the environment is unknown? For example, there is a maze with an unknown destination cell. The agent needs to find the destination cell. The state is 1 or 0, meaning the destination reached or not. But the environment, which is the maze, can only provide the state at cell $$i$$ which is 0 or 1 only when the agent reaches cell $$i$$.

Can this still be solved by RL? I am confused about the environment setup.

• I think you're using the numbers $0$ and $1$ to denote "reward". Is this correct? Btw, I've changed your title to be what I think is your question. Please, make sure that's really your question. If not, feel free to edit your post again.
– nbro
Nov 6, 2020 at 21:12