Robust Reinforcement Learning

Robust Reinforcement Learning is a reinforcement learning framework that extends standard Markov decision processes by explicitly accounting for uncertainties and adversarial perturbations in the environment dynamics or reward structure to ensure stable and reliable policy performance across diverse and non-stationary conditions.

210

Publications

11.4K

Citations

651

Authors

222

Institutions

Publications per year

2017–2026

172

Variants

Robust Rl

Authors

651

  1. SM
    Shie Mannor

    Technion – Israel Institute of Technology, Israel

    11 publications · 334 citations

    11

    h-index

  2. XH
    Xiangkun He

    Nanyang Technological University, Singapore

    7 publications · 498 citations

    7

    h-index

  3. CL
    Chen Lv

    Nanyang Technological University, Singapore

    7 publications · 498 citations

    7

    h-index

  4. TM
    Timothy Mann

    Technion – Israel Institute of Technology, Israel

    6 publications · 123 citations

    6

    h-index

  5. DJ
    Daniel J. Mankowitz

    Technion – Israel Institute of Technology, Israel

    6 publications · 123 citations

    6

    h-index

1–5 of 651

Venues

  1. arXiv (Cornell University)

    Repository · 62 publications · 2.8K citations

    26

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 11 publications · 638 citations

    10

    h-index

  3. IEEE Access

    4 publications · 291 citations

    4

    h-index

  4. Neurocomputing

    4 publications · 101 citations

    4

    h-index

  5. 4

    h-index

1–5