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arXiv · 1308.4828

The Sample-Complexity of General Reinforcement Learning

Tor Lattimore·Marcus Hutter·Peter Sunehag
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Abstract

We present a new algorithm for general reinforcement learning where the true environment is known to belong to a finite class of N arbitrary models. The algorithm is shown to be near-optimal for all but O(N log^2 N) time-steps with high probability. Infinite classes are also considered where we show that compactness is a key criterion for determining the existence of uniform sample-complexity bounds. A matching lower bound is given for the finite case.

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Tor Lattimore, Marcus Hutter, Peter Sunehag. 2013-08-22. The Sample-Complexity of General Reinforcement Learning. https://arxiv.org/abs/1308.4828

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