This work investigates the implementation of the Deep Deterministic Policy Gradient (DDPG) algorithm to enhance the target-reaching capability of the seven degree-of-freedom (7-DoF) Franka Pandarobotic arm. A simulated environment is established by employing OpenAI Gym, PyBullet, and Panda Gym. After 100,000 training time steps, the DDPG algorithm attains a success rate of 100% and an average reward of −1.8. The actor loss and critic loss values are 0.0846 and 0.00486, respectively, indicating improved decision-making and accurate value function estimations. The simulation results demonstrate the efficiency of DDPG in improving robotic arm performance, highlighting its potential for application to improve robotic arm manipulation.

This work presents the implementation of Deep Deterministic Policy Gradient (DDPG) algorithm to enhance target reaching capability of the seven Degree-of-Freedom (7-DoF) Franka Panda robot arm. A simulated environment is established by employing OpenAI Gym, PyBullet, and Panda Gym. Upon completion of 100,000 training time steps, the DDPG algorithm attains a success rate of 100% and an average reward of -1.8. The actor loss and critic loss values are 0.0846 and 0.00486, respectively, indicating improved decision-making and accurate value function estimations. The simulation results demonstrate the efficiency of DDPG in improving robotic arm performance, highlighting its potential for application to improve robot arm manipulation.