Finite-time analysis of the multiarmed bandit problem

Auer, P., Cesa-Bianchi, & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2–3), 2-3.

Type

Journal article

Intellectual base

Primary research

Year

2002

Volume

47

Issue

2–3

Pages

2-3

Link

https://doi.org/10.1023/A:1013689704352verified

Cited from

biology_ecology

Cited by

1 citation across 1 artifact.

Each citation links to the sentence it supports in the citing article.

Primes

• Variation Strategies
  • The bandit framework balances exploitation (continuing with the best-known option) against exploration (testing alternatives), a problem Auer, Cesa-Bianchi, and Fischer (2002) solved with the UCB algorithm by quantifying the optimal exploration premium as a logarithmic function of regret.

    This sourceEstablishes the UCB algorithm and proves logarithmic regret bounds, providing the canonical formal treatment of the exploration–exploitation trade-off in sequential decision-making.

    Supported in partVerified against the work's full text

    The paper's own text frames the multi-armed bandit as the exploration/exploitation dilemma and states that regret must grow logarithmically, but the passage shown does not name UCB or attribute the exploration premium to these authors.

    “One of the simplest examples of the exploration/exploitation dilemma is the multi-armed bandit problem. Lai and Robbins were the first ones to show that the regret for this problem has to grow at least logarithmically in the number of plays. Since then, policies which asymptotically achieve this regret have been devised by Lai and Robbins and many others.”

    Read by an automated reader; not a human review. How support was checked

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