Exploration Via Randomization

Exploration Via Randomization is a stochastic decision-making strategy within the exploration-exploitation tradeoff framework that distinguishes itself by introducing controlled randomness into action selection to systematically sample uncertain options and gather information about the environment.

8

Publications

690

Citations

14

Authors

9

Institutions

Publications per year

2017–2026

5

Authors

14

  1. IO
    Ian Osband

    Stanford University, United States

    3 publications · 270 citations

    3

    h-index

  2. DR
    Daniel Russo

    Columbia University, United States

    3 publications · 203 citations

    3

    h-index

  3. BV
    Benjamin Van Roy

    Stanford University, United States

    3 publications · 270 citations

    3

    h-index

  4. ZW
    Zheng Wen

    Adobe Systems (United States), United States

    3 publications · 270 citations

    3

    h-index

  5. EB
    Elizabeth Bonawitz

    Rutgers, The State University of New Jersey, United States

    2 publications · 253 citations

    2

    h-index

1–5 of 14

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 167 citations

    3

    h-index

  2. Journal of Machine Learning Research

    JMLR · 1 publications · 119 citations

    1

    h-index

  3. The International Journal of Robotics Research

    IJRR · 1 publications · 46 citations

    1

    h-index

  4. Current Opinion in Behavioral Sciences

    1 publications · 234 citations

    1

    h-index

1–4