Dr. Zhangyu Guan Profile

Dr. Zhangyu Guan

at Univ at Buffalo

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 31 May 2022 Paper

On the source-to-target gap of robust double deep Q-learning in digital twin-enabled wireless networks

Maxwell McManus, Zhangyu Guan, Nicholas Mastronarde, Shaofeng Zou

Proceedings Volume 12097, 1209706 (2022) https://doi.org/10.1117/12.2618612

KEYWORDS: Network architectures, Detection and tracking algorithms, Neural networks, Environmental sensing, Computer simulations, Unmanned aerial vehicles, Nanoelectromechanical systems, Device simulation, Statistical analysis

Read Abstract +

Digital twin has been envisioned as a key tool to enable data-driven real-time monitoring and prediction, automated modeling as well as zero-touch control and optimization in next-generation wireless networks. However, because of the mismatch between the dynamics in the source domain (i.e., the digital twin) and the target domain (i.e., the real network), policies generated in source domain by traditional machine learning algorithms may suffer from significant performance degradation when applied in the target domain, i.e., the so-called “source-to-target (S2T) gap” problem. In this work we investigate experimentally the S2T gap in digital twin-enabled wireless networks considering a new class of reinforcement learning algorithms referred to as robust deep reinforcement learning. We first design, based on a combination of double deep Q-learning and an R-contamination model, a robust learning framework to control the policy robustness through adversarial dynamics expected in the target domain. Then we test the robustness of the learning framework over UBSim, an event-driven universal simulator for broadband mobile wireless networks. The source domain is first constructed over UBSim by creating a virtual representation of an indoor testing environment at University at Buffalo, and then the target domain is constructed by modifying the source domain in terms of blockage distribution, user locations, among others. We compare the robust learning algorithm with traditional reinforcement learning algorithms in the presence of controlled model mismatch between the source and target domains. Through experiments we demonstrate that, with proper selection of parameter R, robust learning algorithms can reduce significantly the S2T gap, while they can be either too conservative or explorative otherwise. We observe that robust policy transfer is effective especially for target domains with time-varying blockage dynamics.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years