Graph Reinforcement Learning for Network Control via Bi-Level Optimization

• URL: http://arxiv.org/abs/2305.09129v1
• Date: Tue, 16 May 2023 03:20:22 GMT
• Title: Graph Reinforcement Learning for Network Control via Bi-Level Optimization
• Authors: Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira
• Abstract summary: We argue that data-driven strategies can automate this process and learn efficient algorithms without compromising optimality. 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.
• Score: 37.00510744883984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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