# How to deal with the time delay in reinforcement learning?

I have a question regarding the time delay in reinforcement learning (RL).

In the RL, one has state, reward and action. It is usually assumed that (as far as I understand it) when the action is executed on the system, the state changes immediately and that the new state can then be analysed (influencing the reward) to determine the next action. However, what if there is a time delay in this process. For example, when some action is executed at time $$t_1$$, we can only get its effect on the system at $$t_2$$ (You can imagine a flow: the actuator is in the upstream region and the sensor is in the downstream region, so that there will be a time delay between the action and the state). How do we deal with this time delay in RL?