Multi-objective Q-learning

Multi-Objective Q-Learning is a reinforcement learning algorithm that extends the standard Q-learning framework to handle multiple conflicting objectives by learning a set of value functions or policies that approximate the Pareto optimal frontier in a multi-objective Markov decision process.

79

Publications

3.6K

Citations

306

Authors

142

Institutions

Publications per year

2017–2026

66

Authors

306

  1. AC
    Andrea Castelletti

    Politecnico di Milano, Italy

    3 publications · 82 citations

    3

    h-index

  2. MR
    Marcello Restelli

    Politecnico di Milano, Italy

    3 publications · 82 citations

    3

    h-index

  3. JL
    Junqing Li

    Liaocheng University, China

    4 publications · 269 citations

    3

    h-index

  4. FP
    Francesca Pianosi

    University of Bristol, United Kingdom

    3 publications · 82 citations

    3

    h-index

  5. AN
    Ann Nowé

    Vrije Universiteit Brussel, Belgium

    3 publications · 317 citations

    3

    h-index

1–5 of 306

Venues

  1. Expert Systems with Applications

    7 publications · 309 citations

    6

    h-index

  2. Swarm and Evolutionary Computation

    4 publications · 267 citations

    4

    h-index

  3. 4

    h-index

  4. Neurocomputing

    3 publications · 54 citations

    3

    h-index

  5. Computer Networks

    3 publications · 80 citations

    3

    h-index

1–5