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Reinforcement learning via AIXI Approximation

Veness, Joel; Ng, Kee Siong; Hutter, Marcus; Silver, David


This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian optimality notion for general reinforcement learning agents. Previously, it has been unclear whether the theory of AIXI could motivate the design of practical algorithms. We answer this hitherto open question in the affirmative, by providing the first computationally feasible approximation to the AIXI agent....[Show more]

CollectionsANU Research Publications
Date published: 2010-07
Type: Conference paper


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