Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Asymptotically optimal agents

Date

Authors

Lattimore, Tor

Hutter, Marcus

Publisher

Springer Verlag

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

URI

http://hdl.handle.net/1885/14810

Collections

ANU Research Publications

Type

Conference paper

Book Title

Algorithmic learning theory : 22nd international conference, ALT 2011, Espoo, Finland, October 5-7, 2011 : proceedings

Access Statement

Open Access

DOI

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

Downloads

File

Description

Lattimore and Hutter Asymptotically Optimal Agents 2011.pdf (332.7 KB)

abcd