Deep Reinforcement Learning Based Robotic Arm’s Target Reaching Performance Enhancement
Ldet Honelign, Yoseph Abebe, Young Seok Jung, Abera Tullu, Sunghun Jung · Preprints.org · 2025
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.