Minimum Cost-to-go

Minimum Cost-To-Go is a stochastic optimal control function that, within the context of Stochastic Shortest Path Problems, represents the expected minimum cumulative cost from a given state to reach a goal state under an optimal policy.

22

Publications

1.7K

Citations

49

Authors

29

Institutions

Publications per year

2017–2026

2

Variants

Optimal Cost-to-go

Authors

49

  1. KR
    Krishna R. Pattipati

    University of Connecticut, United States

    3 publications · 299 citations

    3

    h-index

  2. ET
    Edison Tse

    Stanford University, United States

    2 publications · 222 citations

    2

    h-index

  3. YB
    Yaakov Bar‐Shalom

    University of Connecticut, United States

    2 publications · 222 citations

    2

    h-index

  4. BV
    Bart van den Broek

    Radboud University Nijmegen, Netherlands

    2 publications · 93 citations

    2

    h-index

  5. SM
    Steven M. LaValle

    University of Illinois Urbana-Champaign, United States

    2 publications · 68 citations

    2

    h-index

1–5 of 49

Venues

  1. 2

    h-index

  2. Transportation Science

    2 publications · 124 citations

    2

    h-index

  3. IEEE Transactions on Automatic Control

    2 publications · 127 citations

    2

    h-index

  4. Advanced Robotics

    1 publications · 26 citations

    1

    h-index

  5. Uncertainty in Artificial Intelligence

    Conference · 1 publications · 57 citations

    1

    h-index

1–5