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The sample-complexity of general reinforcement learning

Lattimore, Tor; Hutter, Marcus; Sunehag, Peter


We present a new algorithm for general reinforcement learning where the true environment is known to belong to a finite class of N arbitrary models. The algorithm is shown to be near-optimal for all but O(N log2 N) timesteps with high probability. Infinite classes are also considered where we show that compactness is a key criterion for determining the existence of uniform sample-complexity bounds. A matching lower bound is given for the finite case.

CollectionsANU Research Publications
Date published: 2013-06
Type: Conference paper


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