# Reinforcement Learning with Model Predictive Control for Highway Ramp Metering

arXiv: https://arxiv.org/abs/2311.08820 · HTML: https://mlsanity.com/paper/2311.08820/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 80 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 13 of 13)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Bilevel Programming for Hyperparameter Optimization and Meta-Learning](https://mlsanity.com/paper/1806.04910/) (2018)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability](https://mlsanity.com/paper/2107.06277/) (2021)
- [Cautious Model Predictive Control using Gaussian Process Regression](https://mlsanity.com/paper/1705.10702/) (2017)
- [Safe Reinforcement Learning Using Robust MPC](https://mlsanity.com/paper/1906.04005/) (2019)
- [Data-driven Economic NMPC using Reinforcement Learning](https://mlsanity.com/paper/1904.04152/) (2019)
- [Performance-oriented model learning for data-driven MPC design](https://mlsanity.com/paper/1904.10839/) (2019)
- [Probabilistic model predictive safety certification for learning-based control](https://mlsanity.com/paper/1906.10417/) (2019)
- [Expert Level control of Ramp Metering based on Multi-task Deep Reinforcement Learning](https://mlsanity.com/paper/1701.08832/) (2017)
- [Learning for MPC with Stability & Safety Guarantees](https://mlsanity.com/paper/2012.07369/) (2020)
- [Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles](https://mlsanity.com/paper/2008.07240/) (2020)
- [Approximate Robust NMPC using Reinforcement Learning](https://mlsanity.com/paper/2104.02743/) (2021)

## Built on by (most cited first, 4 of 4)

- [Safe model-based Reinforcement Learning via Model Predictive Control and Control Barrier Functions](https://mlsanity.com/paper/2512.04856/) (2025)
- [Second-Order MPC-Based Distributed Q-Learning](https://mlsanity.com/paper/2511.16424/) (2025)
- [Adaptive Tuning of Parameterized Traffic Controllers via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2512.07417/) (2025)
- [LLM-Based Hierarchical Coordinated Control with Continuation-Aware Policy Learning](https://mlsanity.com/paper/2608.15041/) (2026)

## Abstract (arXiv)

In the backdrop of an increasingly pressing need for effective urban and highway transportation systems, this work explores the synergy between model-based and learning-based strategies to enhance traffic flow management by use of an innovative approach to the problem of ramp metering control that embeds Reinforcement Learning (RL) techniques within the Model Predictive Control (MPC) framework. The control problem is formulated as an RL task by crafting a suitable stage cost function that is representative of the traffic conditions, variability in the control action, and violations of the constraint on the maximum number of vehicles in queue. An MPC-based RL approach, which leverages the MPC optimal problem as a function approximation for the RL algorithm, is proposed to learn to efficiently control an on-ramp and satisfy its constraints despite uncertainties in the system model and variable demands. Simulations are performed on a benchmark small-scale highway network to compare the proposed methodology against other state-of-the-art control approaches. Results show that, starting from an MPC controller that has an imprecise model and is poorly tuned, the proposed methodology is able to effectively learn to improve the control policy such that congestion in the network is reduced and constraints are satisfied, yielding an improved performance that is superior to the other controllers.