Batched bandit problems

Vianney Perchet, Philippe Rigollet, Sylvain Chassang, Erik Snowberg

Research output: Contribution to journalArticlepeer-review

97 Scopus citations


Motivated by practical applications, chiefly clinical trials, we study the regret achievable for stochastic bandits under the constraint that the employed policy must split trials into a small number of batches. We propose a simple policy, and show that a very small number of batches gives close to minimax optimal regret bounds. As a byproduct, we derive optimal policies with low switching cost for stochastic bandits.

Original languageEnglish (US)
Pages (from-to)660-681
Number of pages22
JournalAnnals of Statistics
Issue number2
StatePublished - Apr 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Batches
  • Grouped clinical trials, sample size determination, switching cost
  • Multi-armed bandit problems
  • Multi-phase allocation
  • Regret bounds


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