This paper presents a deep reinforcement learning approach for static gain scheduling of a PID controller in a DC motor speed control system. A deep deterministic policy gradient (DDPG) agent learns a direct mapping from the speed reference to fixed PID gains that remain constant during each ten second episode. The action space is constrained by heuristic bounds derived from domain knowledge, ensuring that the proportional, integral, and derivative gains stay within safe ranges. An exponential reward function, which heavily penalises the integrated squared tracking error, is used to force the agent to virtually eliminate the steady state offset. The DC motor model yields a second order overdamped transfer function. After training over 1000 episodes with random references between 0.5 and 2.0 rad/s, the learned gains for a reference of 1.0 rad/s are compared with those obtained from MATLAB’s PID Tuner. The reinforcement learning scheduler achieves approximately 34 % faster rise time, 23 % lower IAE, and 27 % lower ITAE than the classical tuner, although with a larger overshoot (13.1 % versus 7.04 %). Robustness analysis under ±20 % parameter variations shows that the RL controller maintains lower IAE in most scenarios. The training was repeated over five independent runs, showing very low variance (IAE standard deviation: 0.0019 rad·s), which confirms reproducibility. The main limitation is the static policy, where gains are not updated within an episode. The proposed method offers a competitive alternative to classical tuning when high tracking accuracy is required and the operating point changes slowly.