TY - JOUR
T1 - On the computability of Solomonoff induction and AIXI
AU - Leike, Jan
AU - Hutter, Marcus
N1 - Publisher Copyright:
© 2017
PY - 2018/3/15
Y1 - 2018/3/15
N2 - How could we solve the machine learning and the artificial intelligence problem if we had infinite computation? Solomonoff induction and the reinforcement learning agent AIXI are proposed answers to this question. Both are known to be incomputable. We quantify this using the arithmetical hierarchy, and prove upper and in most cases corresponding lower bounds for incomputability. Moreover, we show that AIXI is not limit computable, thus it cannot be approximated using finite computation. However there are limit computable ε-optimal approximations to AIXI. We also derive computability bounds for knowledge-seeking agents, and give a limit computable weakly asymptotically optimal reinforcement learning agent.
AB - How could we solve the machine learning and the artificial intelligence problem if we had infinite computation? Solomonoff induction and the reinforcement learning agent AIXI are proposed answers to this question. Both are known to be incomputable. We quantify this using the arithmetical hierarchy, and prove upper and in most cases corresponding lower bounds for incomputability. Moreover, we show that AIXI is not limit computable, thus it cannot be approximated using finite computation. However there are limit computable ε-optimal approximations to AIXI. We also derive computability bounds for knowledge-seeking agents, and give a limit computable weakly asymptotically optimal reinforcement learning agent.
KW - AIXI
KW - Arithmetical hierarchy
KW - Computability
KW - General reinforcement learning
KW - Knowledge-seeking agents
KW - Solomonoff induction
UR - http://www.scopus.com/inward/record.url?scp=85036534919&partnerID=8YFLogxK
U2 - 10.1016/j.tcs.2017.11.020
DO - 10.1016/j.tcs.2017.11.020
M3 - Article
SN - 0304-3975
VL - 716
SP - 28
EP - 49
JO - Theoretical Computer Science
JF - Theoretical Computer Science
ER -