Distributed Policy Optimization

Distributed Policy Optimization is a distributed reinforcement learning framework that coordinates policy updates across multiple agent-specific learners through decentralized optimization, distinguishing itself from centralized approaches by enabling scalable, asynchronous, and communication-efficient training in multi-robot systems.

14

Publications

619

Citations

53

Authors

24

Institutions

Publications per year

2017–2026

14

Authors

53

  1. AK
    Abhishek Kadian

    Georgia Institute of Technology, United States

    3 publications · 222 citations

    3

    h-index

  2. DP
    Devi Parikh

    Georgia Institute of Technology, United States

    3 publications · 222 citations

    3

    h-index

  3. DB
    Dhruv Batra

    Georgia Institute of Technology, United States

    3 publications · 222 citations

    3

    h-index

  4. AS
    Ari S. Morcos

    Meta (Israel), Israel

    3 publications · 222 citations

    3

    h-index

  5. MS
    Manolis Savva

    Stanford University, United States

    3 publications · 222 citations

    3

    h-index

1–5 of 53

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 254 citations

    5

    h-index

  2. IEEE Transactions on Automatic Control

    2 publications · 60 citations

    2

    h-index

  3. 1

    h-index

  4. Computer-Aided Civil and Infrastructure Engineering

    CACAIE · 1 publications · 29 citations

    1

    h-index

  5. Journal of Computer Science and Technology

    1 publications · 13 citations

    1

    h-index

1–5