Learning to route efficiently with end-to-end feedback : the value of (identifiable) networked structure

In this thesis, we introduce efficient algorithms which achieve nearly optimal instance-dependent and worst case regrets for the problem of stochastic online shortest path routing with end-to-end feedback. The setting is a natural application of the combinatorial stochastic bandits problem, a special case of the linear stochastic bandits problem. We show how the difficulties posed by the large scale action set can be overcome by the networked structure of the action set. Our approach presents a novel connection between bandit learning and shortest path algorithms. Our main contribution is a series of adaptive exploration algorithms that achieves nearly optimal O ((d²ln(T)+d³) [delta]max=[delta]²min) instance-dependent regret and Õ(d [square root]T) worst case regret at the same time. Driven by the carefully designed Top-Two Comparison (TTC) technique, the algorithms are efficiently implementable. We also conduct extensive numerical experiments to show that our proposed algorithms not only achieve superior regret performances, but also reduce the runtime drastically.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Aeronautics and Astronautics.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Zhu, Ruihao, S. M. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Eytan Modiano.

• Aeronautics and Astronautics.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/120384

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/120384