CESG Seminar: Archana Bura

Friday, March 31, 2023
3:50 – 4:50 p.m. (CST)
Zoom (see syllabus or email list for link)

Archana Bura
PhD Candidate, Spring 2023
Dept. of Electrical and Computer Engineering
Texas A&M University

Title: “Constrained Reinforcement Learning for Wireless Networks ”

Talking Points

Challenges in learning under real world problems with constraints
Developing CRL algorithms for two real world resource allocation problems under constraints
Safe exploration in learning a generic Constrained MDP

Abstract
In this talk, I will discuss how we efficiently applied reinforcement learning methods for real world problems. I consider two motivating real world problems: Resource allocation for Media streaming at the Wireless edge, and Resource block allocation in an Open RAN system. Under throughput, latency and resource constraints, these systems can be modeled by Constrained Markov Decision Processes (CMDPs). Since these systems have complex dynamics, a constrained reinforcement learning (CRL) approach is attractive for determining an optimal control policy. Applying off-the-shelf RL algorithms yields better results compared to naive solutions, but these algorithms need a lot of samples to train or have high complexity. We overcome these issues by providing CRL methods that efficiently utilize the structure in the problem. Motivated by these results, we study the fundamental “safe exploration” problem in a generic CRL, and propose a safe RL method that does not violate constraints during the learning process with high probability.

Biography
Archana Bura is a PhD candidate at Texas A&M University’s Department of Electrical and Computer Engineering, where she specializes in constrained reinforcement learning. Her research centers around implementing RL for real world problems, where safety is critical, by developing algorithms and theory of constrained reinforcement learning that leverage structure to enhance the learning process.