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This is a Experimental Study study.

Cascade-Trained Deep Reinforcement Learning for PID Gain Optimization in Precise Joint Position Control of 6-DoF Robotic Arm

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DRL-PID control achieves superior accuracy in tracking arbitrary waypoints, improving overall joint position control.
Key performance metrics such as Mean Absolute Error and Root Mean Square Error validate the effectiveness of the proposed method.
Cascaded training approach addresses overestimation of control parameters, ensuring precise control in robotic applications.
The approach shows promise for broader applicability in complex dynamic systems with variable inputs.

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Cascaded training improves joint position control and outperforms conventional PID in a 6-DoF robotic arm, indicating enhanced performance metrics.

Vyas et al. (2025) studied this question.

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Cascaded training improves joint position control and outperforms conventional PID in a 6-DoF robotic arm, indicating enhanced performance metrics.