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A Monte-Carlo AIXI approximation

Date

Authors

Veness, Joel

Ng, Kee Siong

Hutter, Marcus

Uther, William T.B.

Silver, David

Publisher

Morgan Kauffman Publishers

Abstract

This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. Our approach is based on a direct approximation of AIXI, a Bayesian optimality notion for general reinforcement learning agents. Previously, it

Keywords

Keywords: Context Tree Weighting; MONTE CARLO; Optimality; Practical algorithms; Reinforcement learning agent; Tree search algorithm; Approximation algorithms; Intelligent agents; Plant extracts; Reinforcement learning; Trees (mathematics)

URI

http://hdl.handle.net/1885/68774

Collections

ANU Research Publications

Source

Journal of Artificial Intelligence Research

Type

Journal article

Access Statement

Open Access via publisher website

DOI

10.1613/jair.3125