Solomonoff归纳法的可计算性与知识寻求
On the Computability of Solomonoff Induction and Knowledge-Seeking
Australian National University（澳大利亚国立大学）
摘要：
所罗门诺夫归纳法被认为是学习的黄金标准，但众所周知它是不可计算的。我们量化其不可计算性放置各种口味的Solomonoff的先验M的算术层次。我们还推导了知识寻求代理的可计算性界，并给出了一个有限可计算的弱渐近最优强化学习代理。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
