Chapter

Algorithmic Learning Theory

Volume 6925 of the series Lecture Notes in Computer Science pp 368-382

Asymptotically Optimal Agents

  • Tor LattimoreAffiliated withResearch School of Computer Science, Australian National University
  • , Marcus HutterAffiliated withResearch School of Computer Science, Australian National UniversityETH Zürich

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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