Average Reward Q-learning

Average Reward Q-Learning is a reinforcement learning algorithm that extends the standard Q-Learning framework by optimizing the expected average reward per time step rather than the discounted cumulative reward, thereby focusing on long-term steady-state performance in ergodic Markov decision processes.

43

Publications

1.6K

Citations

118

Authors

62

Institutions

Publications per year

2017–2026

21

Authors

118

  1. SB
    Shalabh Bhatnagar

    Indian Institute of Science Bangalore, India

    2 publications · 101 citations

    3

    h-index

  2. HS
    Hiteshi Sharma

    University of Southern California, United States

    2 publications · 49 citations

    2

    h-index

  3. HL
    Haipeng Luo

    University of Southern California, United States

    2 publications · 49 citations

    2

    h-index

  4. RM
    Rémi Munos

    Google (United States), United States

    2 publications · 168 citations

    2

    h-index

  5. CW
    Chen-Yu Wei

    University of Southern California, United States

    2 publications · 49 citations

    2

    h-index

1–5 of 118

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 63 citations

    4

    h-index

  2. International Conference on Machine Learning

    Conference · 2 publications · 50 citations

    2

    h-index

  3. 1

    h-index

  4. Systems & Control Letters

    1 publications · 22 citations

    1

    h-index

  5. Machine Learning

    1 publications · 96 citations

    1

    h-index

1–5