Hyun Sul Lim refactored the Dreamer agent configuration for the uci_f1tenth_workshop repository, centralizing all Dreamer-related settings into a dedicated constants module to streamline agent initialization and improve reproducibility. Using Python and leveraging ROS and reinforcement learning techniques, Hyun restructured the configuration logic to integrate hyperparameters directly, reducing the risk of configuration errors and eliminating stale workspace or constants leakage. The work focused on cleaning up imports and configuration loading, which enhanced code reliability and experiment consistency. This foundational update prepared the codebase for more efficient training and model configuration workflows, supporting faster iteration and more reliable robotics experiments.