Offline Multi-objective Reinforcement Learning

Offline Multi-Objective Reinforcement Learning is a reinforcement learning framework in which an agent learns a policy that optimally balances multiple competing objectives using a fixed, previously collected dataset of interactions, without further environment exploration.

5

Publications

141

Citations

25

Authors

9

Institutions

Publications per year

2017–2026

5

Authors

25

  1. MK
    Meng‐Lin Ku

    National Central University, Taiwan

    1 publications · 25 citations

    1

    h-index

  2. LZ
    Lei Zhang

    Tsinghua University, China

    1 publications · 21 citations

    1

    h-index

  3. DJ
    Donald J. Bucci

    Lockheed Martin (United States), United States

    1 publications · 28 citations

    1

    h-index

  4. AM
    Adnan Manzoor

    Paderborn University, Germany

    1 publications · 40 citations

    1

    h-index

  5. KZ
    Kewei Zhu

    University of York, United Kingdom

    1 publications · 21 citations

    1

    h-index

1–5 of 25

Venues

  1. IEEE Internet of Things Journal

    1 publications · 25 citations

    1

    h-index

  2. Computer Networks

    1 publications · 27 citations

    1

    h-index

  3. IEEE Transactions on Network and Service Management

    TNSM · 1 publications · 40 citations

    1

    h-index

  4. IEEE/ACM Transactions on Networking

    TON · 1 publications · 21 citations

    1

    h-index

1–4