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Optimistic agents are asymptotically optimal

Date

Authors

Sunehag, Peter

Hutter, Marcus

Publisher

Conference Organising Committee

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

Keywords: Asymptotically optimal; Error bound; Optimality; Optimism; Reinforcement learning agent; Artificial intelligence; Error analysis; Intelligent agents; Reinforcement learning; Optimization Optimality; Optimism; Reinforcement Learning

URI

http://hdl.handle.net/1885/71407

Collections

ANU Research Publications

Source

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Type

Conference paper

Access Statement

Open Access

DOI

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

Restricted until

2037-12-31

Downloads

File

Description

01_Sunehag_Optimistic_agents_are_2012.pdf (234.75 KB)

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