Authors:
Amine Bourki
1
;
Matthieu Coulm
1
;
Philippe Rolet
2
;
Olivier Teytaud
2
and
Paul Vayssière
1
Affiliations:
1
EPITA, France
;
2
TAO, Inria, Umr CNRS 8623, Univ. Paris-Sud, France
Keyword(s):
Simple regret, Automatic parameter tuning, Monte-Carlo tree search.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Planning and Scheduling
;
Simulation and Modeling
;
Symbolic Systems
Abstract:
“Simple regret” algorithms are designed for noisy optimization in unstructured domains. In particular, this literature has shown that the uniform algorithm is indeed optimal asymptotically and suboptimal nonasymptotically. We investigate theoretically and experimentally the application of these algorithms, for automatic parameter tuning, in particular from the point of view of the number of samples required for “uniform” to be relevant and from the point of view of statistical guarantees. We see that for moderate numbers of arms, the possible improvement in terms of computational power required for statistical validation can’t be more than linear as a function of the number of arms and provide a simple rule to check if the simple uniform algorithm (trivially parallel) is relevant. Our experiments are performed on the tuning of a Monte-Carlo Tree Search algorithm, a great recent tool for high-dimensional planning with particularly impressive results for difficult games and in particul
ar the game of Go.
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