I have a use case where the state of the environment could change due to random events in between time steps that the agent takes actions. For example, at t1, the agent takes action a1 and is given the reward and the new state s1. Before the agent takes the next action at t2, some random events occurred in the environment that altered the state. Now when the agent takes action at t2, it's now acting on "stale information" since the state of the environment had changed. Also, the new state s2 will represent changes not only due to the agent's action, but also due to the prior random events that occurred. In the worst case, the action could possibly have become invalid for the new state that was introduced due to these random events occurred within the environment.
How do we deal with this? Does this mean that this use case is not a good one to solve with RF? If we just ignore these changing states due to the random events in the environment, how would that affect the various learning algorithms? I presume that this is not a uncommon or unique problem in real-life use cases...