Censored exploration and the Dark Pool Problem

Authors:
Kuzman Ganchev;Michael Kearns;Yuriy Nevmyvaka;Jennifer Wortman Vaughan
Affiliations:
University of Pennsylvania;University of Pennsylvania;University of Pennsylvania;University of Pennsylvania
Venue:
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Year:
2009

Citing 3
Cited 1

Near-Optimal Reinforcement Learning in Polynomial Time

Machine Learning
R-max - a general polynomial time algorithm for near-optimal reinforcement learning

The Journal of Machine Learning Research
Prediction, Learning, and Games

Prediction, Learning, and Games

Censored exploration and the dark pool problem

Communications of the ACM

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Abstract

We introduce and analyze a natural algorithm for multi-venue exploration from censored data, which is motivated by the Dark Pool Problem of modern quantitative finance. We prove that our algorithm converges in polynomial time to a near-optimal allocation policy; prior results for similar problems in stochastic inventory control guaranteed only asymptotic convergence and examined variants in which each venue could be treated independently. Our analysis bears a strong resemblance to that of efficient exploration/exploitation schemes in the reinforcement learning literature. We describe an extensive experimental evaluation of our algorithm on the Dark Pool Problem using real trading data.