J. Veness, K.S. Ng, M. Hutter, W. Uther and D. Silver (2011) "A Monte-Carlo AIXI Approximation", Volume 40, pages 95-142 Honorable Mention for the 2014 IJCAI-JAIR Best Paper Prize

PDF | PostScript | doi:10.1613/jair.3125

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 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. To develop our approximation, we introduce a new Monte-Carlo Tree Search algorithm along with an agent-specific extension to the Context Tree Weighting algorithm. Empirically, we present a set of encouraging results on a variety of stochastic and partially observable domains. We conclude by proposing a number of directions for future research.

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