Multiobjective Reinforcement Learning

Multiobjective Reinforcement Learning is a reinforcement learning framework that extends the standard Markov decision process to environments with multiple, often competing, objective functions, where the goal is to learn policies that optimize a vector of cumulative rewards rather than a scalar return.

82

Publications

5.2K

Citations

256

Authors

107

Institutions

Publications per year

2017–2026

61

Variants

Morl

Authors

256

  1. PV
    Peter Vamplew

    Federation University, Australia

    9 publications · 889 citations

    9

    h-index

  2. RD
    Richard Dazeley

    Federation University, Australia

    8 publications · 876 citations

    8

    h-index

  3. AN
    Ann Nowé

    Vrije Universiteit Brussel, Belgium

    7 publications · 931 citations

    7

    h-index

  4. DM
    Diederik M. Roijers

    Vrije Universiteit Brussel, Belgium

    5 publications · 609 citations

    5

    h-index

  5. CF
    Cameron Foale

    Federation University, Australia

    4 publications · 113 citations

    4

    h-index

1–5 of 256

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 318 citations

    7

    h-index

  2. Neurocomputing

    5 publications · 184 citations

    5

    h-index

  3. 4

    h-index

  4. IEEE Access

    3 publications · 104 citations

    3

    h-index

  5. Expert Systems with Applications

    3 publications · 75 citations

    3

    h-index

1–5