This work reports the control challenges of first-order stable inverse response systems, which often exhibit oscillations and increased time constants when managed by conventional PI controllers, sometimes leading to instability. To address these issues, a reinforcement learning–based DDPG (RL-DDPG) controller is proposed. The RL agent is trained to control an inverse response system, reducing overshoot and settling time while keeping the system stable. Simulation results demonstrate that the RL-DDPG controller significantly improves performance over traditional PI control by effectively eliminating oscillations and enhancing robustness in inverse response systems. The performance of the RL-DDPG controller is evaluated using different setpoints to demonstrate its robustness and accuracy.