Developed a minimal 2D CARLA environment for deep reinforcement learning within the OpenHUTB/nn repository, focusing on enabling 2D observations, configurable simulation scenes, and streamlined training optimizations. The work emphasized algorithm testing and simulation environment integration, allowing for accelerated RL experimentation and improved data throughput. By implementing clear experiment configuration and 2D sensor modeling, the solution supported reproducibility and laid groundwork for scalable research in autonomous systems. All changes were documented with descriptive commit messages in Markdown, ensuring traceability and auditability. No major bugs were addressed during this period, with efforts concentrated on delivering a robust, lightweight feature foundation.