Abstract
Reinforcement learning (RL) has evolved into a widely investigated technology for the development of smart TSC strategies. However, current RL algorithms necessitate excessive interaction with the environment to learn effective policies, making them impractical for large-scale tasks. The DreamerV3 algorithm presents compelling properties for policy learning. It summarizes general dynamics knowledge about the environment and enables the prediction of future outcomes of potential actions from past experience, reducing the interaction with the environment through imagination training. In this paper, a corridor TSC model is trained using the DreamerV3 algorithm to explore the benefits of world models for TSC strategy learning. In RL environment design, to manage congestion levels effectively, both the state and reward functions are defined based on queue length, and the action is designed to manage queue length efficiently. Using the SUMO simulation platform, the two hyperparameters (train