  The optimal operation of transportation networks is often susceptible to
unexpected disruptions, such as traffic incidents and social events. Many
established control strategies rely on mathematical models that struggle to
cope with real-world uncertainties, leading to a significant decline in
effectiveness when faced with substantial disruptions. While previous research
works have dedicated efforts to improving the robustness or resilience of
transportation systems against disruptions, this paper applies the cutting-edge
concept of antifragility to better design a traffic control strategy for urban
road networks. Antifragility sets itself apart from robustness and resilience
as it represents a system's ability to not only withstand stressors, shocks,
and volatility but also thrive and enhance performance in the presence of such
adversarial events. Hence, modern transportation systems call for solutions
that are antifragile. In this work, we propose a model-free deep Reinforcement
Learning (RL) scheme to control a two-region urban traffic perimeter network.
The system exploits the learning capability of RL under disruptions to achieve
antifragility. By monitoring the change rate and curvature of the traffic state
with the RL framework, the proposed algorithm anticipates imminent disruptions.
An additional term is also integrated into the RL algorithm as redundancy to
improve the performance under disruption scenarios. When compared to a
state-of-the-art model predictive control approach and a state-of-the-art RL
algorithm, our proposed method demonstrates two antifragility-related
properties: (a) gradual performance improvement under disruptions of constant
magnitude; and (b) increasingly superior performance under growing disruptions.
