IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v18y2022i7p15501329221114546.html
   My bibliography  Save this article

A non-asymptotic analysis of adaptive TD(λ) learning in wireless sensor networks

Author

Listed:
  • Bing Li
  • Tao Li
  • Muhua Liu
  • Junlong Zhu
  • Mingchuan Zhang
  • Qingtao Wu

Abstract

Wireless sensor network has been widely used in different fields, such as structural health monitoring and artificial intelligence technology. The routing planning, an important part of wireless sensor network, can be formalized as an optimization problem needing to be solved. In this article, a reinforcement learning algorithm is proposed to solve the problem of optimal routing in wireless sensor networks, namely, adaptive TD( λ ) learning algorithm referred to as ADTD( λ ) under Markovian noise, which is more practical than i.i.d. (identically and independently distributed) noise in reinforcement learning. Moreover, we also present non-asymptotic analysis of ADTD( λ ) with both constant and diminishing step-sizes. Specifically, when the step-size is constant, the convergence rate of O ( 1 / T ) is achieved, where T is the number of iterations; when the step-size is diminishing, the convergence rate of O ~ ( 1 / T ) is also obtained. In addition, the performance of the algorithm is verified by simulation.

Suggested Citation

  • Bing Li & Tao Li & Muhua Liu & Junlong Zhu & Mingchuan Zhang & Qingtao Wu, 2022. "A non-asymptotic analysis of adaptive TD(λ) learning in wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 18(7), pages 15501329221, July.
    • Handle: RePEc:sae:intdis:v:18:y:2022:i:7:p:15501329221114546
    DOI: 10.1177/15501329221114546
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/15501329221114546
    Download Restriction: no
    File URL: https://libkey.io/10.1177/15501329221114546?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:intdis:v:18:y:2022:i:7:p:15501329221114546. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.