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Graph Reinforcement Learning for Network Control via Bi-Level Optimization
- Publication Year :
- 2023
-
Abstract
- Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional optimization-based approaches do not scale to large networks, and (2) the design of good heuristics or approximation algorithms often requires significant manual trial-and-error. In this work, we argue that data-driven strategies can automate this process and learn efficient algorithms without compromising optimality. To do so, we present network control problems through the lens of reinforcement learning and propose a graph network-based framework to handle a broad class of problems. Instead of naively computing actions over high-dimensional graph elements, e.g., edges, we propose a bi-level formulation where we (1) specify a desired next state via RL, and (2) solve a convex program to best achieve it, leading to drastically improved scalability and performance. We further highlight a collection of desirable features to system designers, investigate design decisions, and present experiments on real-world control problems showing the utility, scalability, and flexibility of our framework.<br />9 pages, 4 figures
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Optimization and Control (math.OC)
FOS: Electrical engineering, electronic engineering, information engineering
FOS: Mathematics
Systems and Control (eess.SY)
Electrical Engineering and Systems Science - Systems and Control
Mathematics - Optimization and Control
Machine Learning (cs.LG)
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....340ab602cd9393b37c18d4bd63a582a3