Asymptotically Optimal Agents
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Author / Producer
Date
2011
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
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Rights / License
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.
Permanent link
Publication status
published
External links
Book title
Algorithmic Learning Theory
Journal / series
Volume
6925
Pages / Article No.
368 - 382
Publisher
Springer
Event
22nd International Conference on Algorithmic Learning Theory (ALT 2011)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Rational agents; Sequential decision theory; Artificial general intelligence; Reinforcement learning; Asymptotic optimality; General discounting
Organisational unit
03659 - Buhmann, Joachim M. (emeritus) / Buhmann, Joachim M. (emeritus)