Exploration-exploitation Tradeoff

The Exploration-Exploitation Tradeoff is a fundamental decision-making dilemma in reinforcement learning and Markov Decision Processes, where an agent must balance between exploiting known actions to maximize immediate rewards and exploring uncertain actions to acquire potentially superior long-term outcomes.

5K

Publications

397.9K

Citations

11K

Authors

2.3K

Institutions

Publications per year

2017–2026

3K

Authors

11K

  1. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    46 publications · 7.2K citations

    31

    h-index

  2. SL
    Sergey Levine

    University of California, Berkeley, United States

    39 publications · 3.5K citations

    30

    h-index

  3. RM
    Rémi Munos

    Google (United States), United States

    25 publications · 3.1K citations

    20

    h-index

  4. ES
    Eric Schulz

    University College London, United Kingdom

    23 publications · 3.1K citations

    17

    h-index

  5. AA
    Alekh Agarwal

    Microsoft (United States), United States

    23 publications · 1.2K citations

    17

    h-index

1–5 of 11K

Venues

  1. arXiv (Cornell University)

    Repository · 688 publications · 39.1K citations

    89

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 112 publications · 5.5K citations

    41

    h-index

  3. Expert Systems with Applications

    55 publications · 3.3K citations

    32

    h-index

  4. IEEE Access

    51 publications · 2.4K citations

    28

    h-index

  5. 26

    h-index

1–5