Potential-based Reward Shaping

Potential-Based Reward Shaping is a reinforcement learning technique that modifies the reward function by adding state-dependent and next-state-dependent potential differences to accelerate learning while preserving the optimal policy.

28

Publications

1.7K

Citations

71

Authors

39

Institutions

Publications per year

2017–2026

11

Variants

Pbrs

Authors

71

  1. SD
    Sam Devlin

    University of York, United Kingdom

    8 publications · 586 citations

    8

    h-index

  2. DK
    Daniel Kudenko⋆

    University of York, United Kingdom

    7 publications · 449 citations

    7

    h-index

  3. AN
    Ann Nowé

    Vrije Universiteit Brussel, Belgium

    3 publications · 132 citations

    3

    h-index

  4. MG
    Marek Grześ

    University of Kent, United Kingdom

    3 publications · 186 citations

    3

    h-index

  5. AH
    Anna Harutyunyan

    Vrije Universiteit Brussel, Belgium

    2 publications · 111 citations

    2

    h-index

1–5 of 71

Venues

  1. 2

    h-index

  2. The Knowledge Engineering Review

    2 publications · 84 citations

    2

    h-index

  3. arXiv (Cornell University)

    Repository · 1 publications · 94 citations

    1

    h-index

  4. Robotics

    1 publications · 21 citations

    1

    h-index

  5. Mathematics

    1 publications · 47 citations

    1

    h-index

1–5