As part of data science course I would like to solve particular problem with reinforcement learning algorithm.
I believe I understand general concept, however the problems I had read about up till now had no random factor included. I would like to ask if it's valid to assume that reinforcement learning algorithm will be able to find "good" (somewhat close to optimal) solution when, at each step, one or more random events can occur, provided that we know the probability of those events?
For practical example that I can think of: let's say we have to find exit from labyrinth as quick as possible, on each turn we can decide to rest or move. If we move, we become more tired, which increases probability that we will fall and twist our ankle (if this happens our speed is reduced for several turns). We know what's the probability of falling on each turn, but we do not know if this will really happen or not. During rest we reduce probability of falling.
In my project I would like to include several (around 5) of such random events.
Is it correct to assume that reinforcement learning algorithm should be able to find a valid solution, despite such random factors? We can define "valid" as, for example, in 90% of cases taking less than X turns. If yes, I would highly appreciate if you could point me in right direction, or to any materials describing practical solutions to such problems.
Thank you in advance for your input and sorry in case this question is really basic (hopefully not)!