A Reinforcement Learning Approach for Parameter-Free Temperature Control in Large-Area Blackbody Systems Due to the sensitive nature of applications involving large-area blackbody radiation sources, the datasets and source code cannot be made public due to confidentiality requirements. Based on our experience, reinforcement learning algorithms exhibit lower temperature control precision and cannot compare with PID-based methods (which can currently achieve a control accuracy of 0.001 K); furthermore, their training and testing are more cumbersome, although they do offer advantages in multi-channel temperature rate control. The optimal solution would be to employ PID for core temperature control while integrating a multi-channel rate control module to facilitate temperature data communication across channels, thereby adjusting the PWM output of each channel and accelerating the heating rate of the overall surface. In practice, the physical system currently deployed still utilizes the PID temperature control algorithm, and the performance metrics presented reflect the optimal results achieved using PID.