6883e621_ws___Machine_Learning__Theory___3
[0] RL, MDP learning [1] Learning is about finding and acting according to a near optimal policy in an unknown Markov Decision Process. [2] We can learn and act with an amount of summed regret related to O(SA) where S is the number of states and A is the number of actions per state. [3] 
Has anyone counted the number of states in real world problems? We can’t afford to wait that long. Discretizing the states creates a POMDP (see below). In the real world, we often have to deal with a POMDP anyways.
All Models of Learning have Flaws « Machine Learning (Theory)
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