I am trying to implement reinforcement learning into my real-world problem. One thing making me hesitant to apply RL is that this real-world problem of mine is unique in a way how every state is independent of one another. The action taken by the agent at timestep t is the only thing that affects the state at the next timestep. (For example, in the cycle of "state-action-reward-next state", the "next state" is solely dependent on the "action" but not the "state".)
I am wondering if the RL could still be able to learn through this scenario. If not, what other methods could be an option?