Reinforcement learning from comparisons: Three alternatives is enough, two is not

Benoit Laslier; Jean-Francois Laslier

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arXiv · 1301.5734

Reinforcement learning from comparisons: Three alternatives is enough, two is not

Benoit Laslier·Jean-Francois Laslier

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Abstract

The paper deals with the problem of finding the best alternatives on the basis of pairwise comparisons when these comparisons need not be transitive. In this setting, we study a reinforcement urn model. We prove convergence to the optimal solution when reinforcement of a winning alternative occurs each time after considering three random alternatives. The simpler process, which reinforces the winner of a random pair does not always converges: it may cycle.

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Benoit Laslier, Jean-Francois Laslier. 2013-01-24. Reinforcement learning from comparisons: Three alternatives is enough, two is not. https://arxiv.org/abs/1301.5734

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