We have a music player that has different playlists and automatically suggests songs from the current playlist I'm in. What I want the program to learn is, that if I skip the song, it should decrease the probability to be played in this playlist again. I think this is what's called reinforcement learning and I've read a bit about the algorithms, deciding that Markov decision process (MDP) seems to be exactly what we have here. I know that in MDP there is more than one state, so I figured for this case it would mean the different playlists. For example, depending on the state (playlist) I'm in, it chooses the songs that it thinks fits the best and get "punished" (by skipping) if it has chosen wrongly.
Do you guys think this is the right approach? Or would you suggest a different algorithm? Does all of this even make any sense, should I provide more information?
If it does sound right, I'd like to ask for some tutorials or starting points getting about MDP in R. I've searched online but have only found the MDP toolbox in R and it kind of doesn't really make sense to me. Do you have any suggestions?