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]
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