Demonstration-based Reinforcement Learning

Demonstration-Based Reinforcement Learning is a reinforcement learning paradigm that belongs to the class of imitation learning methods, distinguished by its use of expert demonstrations to guide policy acquisition through reward shaping or direct policy initialization.

81

Publications

4.1K

Citations

241

Authors

86

Institutions

Publications per year

2017–2026

49

Authors

241

  1. SC
    Sonia Chernova

    Georgia Institute of Technology, United States

    5 publications · 559 citations

    5

    h-index

  2. ME
    Matthew E. Taylor

    Washington State University, United States

    5 publications · 369 citations

    5

    h-index

  3. HB
    Halit Bener Suay

    Worcester Polytechnic Institute, United States

    4 publications · 339 citations

    4

    h-index

  4. AS
    Adish Singla

    Max Planck Institute for Software Systems, Germany

    4 publications · 117 citations

    4

    h-index

  5. ML
    Marc Lanctot

    Google DeepMind (United Kingdom), United Kingdom

    3 publications · 1.2K citations

    3

    h-index

1–5 of 241

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 761 citations

    10

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 6 publications · 953 citations

    6

    h-index

  3. IEEE Robotics and Automation Letters

    2 publications · 59 citations

    2

    h-index

  4. Adaptive Agents and Multi-Agents Systems

    Conference · 2 publications · 85 citations

    2

    h-index

  5. Artificial Intelligence

    2 publications · 65 citations

    2

    h-index

1–5