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We consider apprenticeship learning, i.e., having an agent learn a task byobserving an expert demonstrating the task in a partially observableenvironment when the model of the environment is uncertain. This setting isuseful in applications where the explicit modeling of the environment isdifficult, such as a dialogue system. We show that we can extract informationabout the environment model by inferring action selection process behind thedemonstration, under the assumption that the expert is choosing optimal actionsbased on knowledge of the true model of the target environment. Proposedalgorithms can achieve more accurate estimates of POMDP parameters and betterpolicies from a short demonstration, compared to methods that learns only fromthe reaction from the environment.

Apprenticeship Learning for Model Parameters of Partially Observable Environments