Reinforcement Learning State Aggregation

Reinforcement Learning State Aggregation is a method in reinforcement learning that partitions the state space into aggregates or groups of states to reduce complexity by generalizing value estimates across states within the same aggregate, thereby improving learning efficiency and scalability.

22

Publications

1.3K

Citations

54

Authors

24

Institutions

Publications per year

2017–2026

4

Authors

54

  1. BV
    Benjamin Van Roy

    Stanford University, United States

    2 publications · 113 citations

    2

    h-index

  2. SS
    Satinder Singh

    University of Michigan, United States

    1 publications · 269 citations

    1

    h-index

  3. VS
    Vivek S. Borkar

    Tata Institute of Fundamental Research, India

    1 publications · 26 citations

    1

    h-index

  4. WU
    William Uther

    UNSW Sydney, Australia

    1 publications · 151 citations

    1

    h-index

  5. DN
    Daniel Nikovski

    Mitsubishi Electric (United States), United States

    1 publications · 13 citations

    1

    h-index

1–5 of 54

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 151 citations

    5

    h-index

  2. Science China Chemistry

    1 publications · 19 citations

    1

    h-index

  3. Information Sciences

    1 publications · 34 citations

    1

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 36 citations

    1

    h-index

  5. CaltechAUTHORS (California Institute of Technology)

    Repository · 1 publications · 11 citations

    1

    h-index

1–5