• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • News
  • Seminars
    • CESG Seminars
    • Fishbowl Seminar Series
    • Computer Engineering Eminent Scholar Seminar Series
    • Topics In Systems Seminar
    • Related Seminars
  • People
    • Faculty
    • Staff
    • Current Visitors
    • Students
  • Research
  • Academics
    • Graduate
    • Undergraduate
  • All Courses
  • Contact
  • Information
    • Information
    • Technology Resources
    • Directions

Computer Engineering and Systems Group

Texas A&M University College of Engineering

CESG SEMINAR: Dr. Siva Theja Maguluri

Posted on March 24, 2022 by Vickie Winston

Friday, April 1, 2022
4:10 – 5:00 p.m.
Zoom: https://tamu.zoom.us/j/96343481647
 
Dr. Siva Theja Maguluri
Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech

Talking Points

  • Lyapunov methods for Stochastic Approximation and Reinforcement learning
  • Generalized Moreau Envelop based on infimal convolution smoothing as a Lyapunov function for Stochastic Approximation of contractive operators
  • Unified framework to obtain sample complexity of a large class of RL algorithms
  • Linear speedup in the number of agents for Federated Reinforcement learning

Title
“A Lyapunov Theory of Finite-Sample Guarantees of Stochastic Approximation and Reinforcement Learning”

Abstract
The focus of our work is to obtain finite-sample and/or finite-time convergence bounds of various model-free Reinforcement Learning (RL) algorithms. Many RL algorithms involve solving the Bellman fixed point equation, which is done using Stochastic Approximation (SA). SA is a popular approach for solving fixed point equations when the information is corrupted by noise. We develop a Lyapunov framework and obtain mean square error bounds on the convergence of a general class of SA algorithms for contractive operators under general norms and Markovian noise. The key tool we use is generalized Moreau envelope as a smooth potential/ Lyapunov function. These powerful results immediately provide sample complexity results of a large class of RL algorithms including TD learning, Q-learning, actor-critic algorithms, their off-policy variants, and their distributed variants. The talk will present a couple of these applications in off-policy RL and/or Federated RL.

Biography
Siva Theja Maguluri is Fouts Family Early Career Professor and Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He obtained his Ph.D. and MS in ECE as well as MS in Applied Math from UIUC, and B.Tech in Electrical Engineering from IIT Madras. His research interests span the areas of Control, Optimization, Algorithms and Applied Probability. In particular, he works on Reinforcement Learning theory, scheduling, resource allocation and revenue optimization problems that arise in a variety of systems including Data Centers, Cloud Computing, Wireless Networks, Block Chains, Ride hailing systems, etc. His research and teaching are recognized through several awards including the  “Best Publication in Applied Probability” award, NSF CAREER award, second place award at INFORMS JFIG best paper competition, Student best paper award at IFIP Performance, “CTL/BP Junior Faculty Teaching Excellence Award,” and “Student Recognition of Excellence in Teaching: Class of 1934 CIOS Award.”


Dr. Maguluri: https://sites.google.com/site/sivatheja/

On Zoom @ 4:10 p.m. on Friday, 4/1/22

Join Zoom Meeting
https://tamu.zoom.us/j/96343481647
Meeting ID: 963 4348 1647

Filed Under: Uncategorized

Recent NEWS

  • CESG Seminar: Peipei Zhou March 1, 2023
  • CESG Seminar – Desik Rengarajan February 21, 2023
  • CESG Seminar – Manoranjan Majji February 13, 2023
  • CESG Seminar – Jiang Hu February 2, 2023
  • CESG Seminar – Sabit Ekin January 24, 2023
  • Congratulations Dr. Hu! January 13, 2023
  • Congratulations Fall 2022 Graduates! December 12, 2022

© 2016–2023 Computer Engineering and Systems Group Log in

Texas A&M Engineering Experiment Station Logo
  • State of Texas
  • Open Records
  • Risk, Fraud & Misconduct Hotline
  • Statewide Search
  • Site Links & Policies
  • Accommodations
  • Environmental Health, Safety & Security
  • Employment