Asymptotically Optimal Agents

  • Tor Lattimore
  • Marcus Hutter
Conference paper

DOI: 10.1007/978-3-642-24412-4_29

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6925)
Cite this paper as:
Lattimore T., Hutter M. (2011) Asymptotically Optimal Agents. In: Kivinen J., Szepesvári C., Ukkonen E., Zeugmann T. (eds) Algorithmic Learning Theory. ALT 2011. Lecture Notes in Computer Science, vol 6925. Springer, Berlin, Heidelberg

Abstract

Artificial general intelligence aims to create agents capable of learning to solve arbitrary interesting problems. We define two versions of asymptotic optimality and prove that no agent can satisfy the strong version while in some cases, depending on discounting, there does exist a non-computable weak asymptotically optimal agent.

Keywords

Rational agents sequential decision theory artificial general intelligence reinforcement learning asymptotic optimality general discounting 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tor Lattimore
    • 1
  • Marcus Hutter
    • 1
    • 2
  1. 1.Research School of Computer ScienceAustralian National UniversityAustralia
  2. 2.ETH ZürichAustralia

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