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Autonomy Talks - 04/03/2025 Speaker: Prof. Filipe Rodrigues, DTU Title: Reinforcement Learning for Network Optimization in Transportation Abstract: Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world transportation problems. However, traditional optimization-based approaches do not scale to large networks, and the design of good heuristics or approximation algorithms often requires significant manual trial-and-error. In this talk, I argue that data-driven strategies can automate this process and learn efficient algorithms without compromising optimality. To do so, I will introduce network control problems through the lens of reinforcement learning and propose a bi-level graph-network-based framework, 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. The benefits of the proposed approach will be demonstrated for Autonomous-Mobility-on-Demand rebalancing and dynamic pricing, and inventory management applications. I will then introduce a framework for offline RL of these bi-level/hierarchical from datasets generated by arbitrary behavior policies (historical data). The learned policies are shown to significantly outperform end-to-end offline RL in terms of performance and robustness.