Filippo Airaldi (2023): Source code for the publication: Reinforcement Learning with Model Predictive Control for Highway Ramp Metering. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/d5074606-82f4-40e9-93cd-cf27f22e501d.v1

Software

x choose version: version 2 - 2024-10-23 (latest)

• Electrical and Electronic Engineering
• Other Engineering
• Engineering

Model Predictive Control, Ramp Metering, Reinforcement Learning

GPL-3.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Filippo Airaldi

Source code for the implementation and simulation of a learning-based ramp metering control strategy with the goal of improving highway traffic flow management, where the proposed solution embeds model-based Reinforcement Learning methodologies in a Model Predictive Control framework, thus enabling the adaptation of the controller in order to improve automatically its performance based solely on observed closed-loop data. Simulations on a highway network benchmark demonstrate significant reduction in congestion and improved constraint satisfaction compared to an imprecise, non-learning initial controller, showcasing the efficacy of the proposed methodology.

• 2023-12-12 first online, published, posted

4TU.ResearchData

source code (.py) and compressed simulation results (.xz)

• https://github.com/FilippoAiraldi/mpcrl-for-ramp-metering
• https://arxiv.org/abs/2311.08820

• CLariNet (grant code 101018826) [more info...] European Research Council

TU Delft, Faculty of Mechanical, Maritime and Materials Engineering, Delft Center for Systems and Control