Graph Reinforcement Learning for Network Control via Bi-Level Optimization

Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira

Keywords: Graph reinforcement learning, Network control, Bi-level optimization

Abstract: Dynamic network flow models have been extensively studied and widely used in the past decades to formulate many problems with great real-world impact, such as transportation, supply chain management, power grid control, and more. Within this context, time-expansion techniques currently represent a generic approach for solving control problems over dynamic networks. However, the complexity of these methods does not allow traditional approaches to scale to large networks, especially when these need to be solved recursively over a receding horizon (e.g., to yield a sequence of actions in model predictive control). Moreover, tractable optimization-based approaches are often limited to simple linear deterministic settings and are not able to handle environments with stochastic, non-linear, or unknown dynamics. In this work, we present dynamic network flow problems through the lens of reinforcement learning and propose a graph network-based framework that can handle a wide variety of problems and learn efficient algorithms without significantly compromising optimality. Instead of a naive and poorly-scalable formulation, in which agent actions (and thus network outputs) consist of actions on edges, we present a two-phase decomposition. The first phase consists of an RL agent specifying desired outcomes to the actions. The second phase exploits the problem structure to solve a convex optimization problem and achieve (as best as possible) these desired outcomes. This formulation leads to dramatically improved scalability and performance. We further highlight a collection of features that are potentially desirable to system designers, investigate design decisions, and present experiments showing the utility, scalability, and flexibility of our framework.

Type Of Submission: Extended abstract (max 4 main pages).

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Type Of Submission: Extended abstract.