Learning Efficiently Function Approximation for Contextual MDP
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We study learning contextual MDPs using a function approximation for both the rewards and the dynamics. We consider both the case where the dynamics is known and unknown, and the case that the dynamics dependent or independent of the context. For all four models we derive polynomial sample and time complexity (assuming an efficient ERM oracle). Our methodology gives a general reduction from learning contextual MDP to supervised learning.
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