Approximate State Aggregation

Approximate State Aggregation is a model approximation technique in reinforcement learning that partitions the state space into aggregates based on similarity or proximity, relaxing exact equivalence requirements to enable scalable and efficient value estimation.

14

Publications

443

Citations

28

Authors

19

Institutions

Publications per year

2017–2026

2

Authors

28

  1. IN
    Illah Nourbakhsh

    Carnegie Mellon University, United States

    2 publications · 46 citations

    2

    h-index

  2. FB
    Frank Broz

    Heriot-Watt University, United Kingdom

    1 publications · 33 citations

    1

    h-index

  3. SC
    Shih-Fen Cheng

    Singapore Management University, Singapore

    1 publications · 62 citations

    1

    h-index

  4. DP
    Dimitri P. Bertsekas

    Massachusetts Institute of Technology, United States

    1 publications · 10 citations

    1

    h-index

  5. HC
    Hoong Chuin Lau

    Singapore Management University, Singapore

    1 publications · 62 citations

    1

    h-index

1–5 of 28

Venues

  1. Transportation Science

    1 publications · 62 citations

    1

    h-index

  2. Advances in Operations Research

    1 publications · 23 citations

    1

    h-index

  3. IEEE Transactions on Automatic Control

    1 publications · 43 citations

    1

    h-index

  4. arXiv (Cornell University)

    Repository · 1 publications · 10 citations

    1

    h-index

  5. International Journal of Robust and Nonlinear Control

    IJRCEA · 1 publications · 23 citations

    1

    h-index

1–5