Abstract
We study a problem inspired from robotics in which we want to find an optimal policy to learn a Partially Observable Markov Decision Process (POMDP) when the agent only has an imperfect model of its environment. To help the agent in its task we assume the availability of an external operator (an oracle), that can provide information about the underlying state. We present the algorithm MEDUSA, which improves an initial POMDP model using experimentation through the environment and a minimum number of queries. We also show how MEDUSA handles non-stationary environments and how it can withstand noise in the query answer.
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