Chapter

Artificial General Intelligence

Volume 7716 of the series Lecture Notes in Computer Science pp 312-321

Optimistic AIXI

  • Peter SunehagAffiliated withResearch School of Computer Science, Australian National University
  • , Marcus HutterAffiliated withResearch School of Computer Science, Australian National University

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Abstract

We consider extending the AIXI agent by using multiple (or even a compact class of) priors. This has the benefit of weakening the conditions on the true environment that we need to prove asymptotic optimality. Furthermore, it decreases the arbitrariness of picking the prior or reference machine. We connect this to removing symmetry between accepting and rejecting bets in the rationality axiomatization of AIXI and replacing it with optimism. Optimism is often used to encourage exploration in the more restrictive Markov Decision Process setting and it alleviates the problem that AIXI (with geometric discounting) stops exploring prematurely.

Keywords

AIXI Reinforcement Learning Optimism Optimality