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On the Computability of Solomonoff Induction and Knowledge-Seeking
Published 15 Jul 2015 in cs.AI and cs.LG | (1507.04124v1)
Abstract: Solomonoff induction is held as a gold standard for learning, but it is known to be incomputable. We quantify its incomputability by placing various flavors of Solomonoff's prior M in the arithmetical hierarchy. We also derive computability bounds for knowledge-seeking agents, and give a limit-computable weakly asymptotically optimal reinforcement learning agent.
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