Learning Algorithms for Automata with Observations
نویسندگان
چکیده
We consider the problem of learning the behavior of a POMDP (Partially Observable Markov Decision Process) with deterministic actions and observations. This is a challenging problem due to the fact that the observations can only partially identify the states. Recent work by Holmes and Isbell offers an approach for inferring the hidden states from experience in deterministic POMDP environments. We propose an alternative algorithm that ensures more accurate predictions, and we show that in fact it produces the minimal predicting machine.
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