Multi-objective Temporal Difference Learning

Multi-Objective Temporal Difference Learning is a reinforcement learning method that extends classical Temporal Difference Learning to handle multiple conflicting objectives by learning a set of value functions or policies that approximate the Pareto optimal frontier in stochastic environments modeled as Multi-Objective Markov Decision Processes.

13

Publications

1.6K

Citations

22

Authors

17

Institutions

Publications per year

2017–2026

2

Authors

22

  1. GT
    Gerald Tesauro

    IBM (United States), United States

    2 publications · 839 citations

    2

    h-index

  2. JN
    John N. Tsitsiklis

    Massachusetts Institute of Technology, United States

    2 publications · 172 citations

    2

    h-index

  3. AN
    Ann Nowé

    Vrije Universiteit Brussel, Belgium

    2 publications · 276 citations

    2

    h-index

  4. BV
    Benjamin Van Roy

    Stanford University, United States

    2 publications · 172 citations

    2

    h-index

  5. MP
    Mohsen Paniri

    Lorestan University, Iran

    1 publications · 128 citations

    1

    h-index

1–5 of 22

Venues

  1. Machine Learning

    2 publications · 839 citations

    2

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  2. Applied Soft Computing

    1 publications · 68 citations

    1

    h-index

  3. Evolving Systems

    1 publications · 18 citations

    1

    h-index

  4. Automatica

    1 publications · 142 citations

    1

    h-index

  5. Bulletin of Mathematical Biology

    1 publications · 22 citations

    1

    h-index

1–5