  Reinforcement learning (RL) is gaining popularity as an effective approach
for traffic signal control (TSC) and is increasingly applied in this domain.
However, most existing RL methodologies are confined to a single-stage TSC
framework, primarily focusing on selecting an appropriate traffic signal phase
at fixed action intervals, leading to inflexible and less adaptable phase
durations. To address such limitations, we introduce a novel two-stage TSC
framework named DynamicLight. This framework initiates with a phase control
strategy responsible for determining the optimal traffic phase, followed by a
duration control strategy tasked with determining the corresponding phase
duration. Experimental results show that DynamicLight outperforms
state-of-the-art TSC models and exhibits exceptional model generalization
capabilities. Additionally, the robustness and potential for real-world
implementation of DynamicLight are further demonstrated and validated through
various DynamicLight variants. The code is released at
https://github.com/LiangZhang1996/DynamicLight.
