# Antifragile perimeter control: Thriving on disruptions through reinforcement learning

arXiv: https://arxiv.org/abs/2402.12665 · HTML: https://mlsanity.com/paper/2402.12665/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 72 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Distributed Prioritized Experience Replay](https://mlsanity.com/paper/1803.00933/) (2018)
- [Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control](https://mlsanity.com/paper/1903.04527/) (2019)
- [Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures](https://mlsanity.com/paper/1904.08353/) (2019)
- [Dynamic optimal congestion pricing in multi-region urban networks by application of a Multi-Layer-Neural network](https://mlsanity.com/paper/2107.14585/) (2021)
- [Antifragile Control Systems: The case of mobile robot trajectory tracking in the presence of uncertainty](https://mlsanity.com/paper/2302.05117/) (2023)
- [Fragility, Robustness and Antifragility in Deep Learning](https://mlsanity.com/paper/2312.09821/) (2023)
- [The fragile nature of road transportation networks](https://mlsanity.com/paper/2402.00924/) (2024)
- [Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network](https://mlsanity.com/paper/2002.01571/) (2020)

## Abstract (arXiv)

The optimal operation of transportation systems is often susceptible to unexpected disruptions. Many established control strategies reliant on mathematical models can struggle with real-world disruptions, leading to significant divergence from their anticipated efficiency. This study integrates the cutting-edge concept of antifragility with learning-based traffic control strategies to optimize urban road network operations under disruptions. Antifragile systems not only withstand and recover from stressors but also thrive and enhance performance in the presence of such adversarial events. Incorporating antifragile modules composed of traffic state derivatives and redundancy, a deep reinforcement learning algorithm is developed. Subsequently, it is evaluated in a cordon-shaped transportation network and a case study with real-world data. Promising results highlight that the proposed algorithm provides: (i) superior performance achieving up to 27.6% and 41.9% performance gain over baselines under increasing demand and supply disruptions, (ii) lower distribution skewness under disruptions, demonstrating its relative antifragility against baselines, (iii) effectiveness under limited observability due to real-world data availability constraints, and (iv) the robustness and transferability to be combined with various state-of-the-art RL frameworks. The proposed antifragile methodology is generalizable and holds potential for applications beyond traffic engineering, offering integration into control systems exposed to disruptions across various disciplines.