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.

Optimistic agents are asymptotically optimal

Date

Authors

Sunehag, Peter

Hutter, Marcus

Publisher

Springer Verlag

Abstract

We use optimism to introduce generic asymptotically optimal reinforcement learning agents. They achieve, with an arbitrary finite or compact class of environments, asymptotically optimal behavior. Furthermore, in the finite deterministic case we provide finite error bounds.

Keywords

Reinforcement Learning, Optimism, Optimality

URI

http://hdl.handle.net/1885/14734

Collections

ANU Research Publications

Type

Conference paper

Book Title

AI 2012: Advances in Artificial Intelligence: 25th Australasian Joint Conference, Sydney, Australia, December 4-7, 2012. Proceedings

DOI

10.1007/978-3-642-35101-3_2

Downloads

File

Description

Sunehag and Hutter Optimistic Agents are Asymptotically Optimal 2012.pdf (125.76 KB)