Research of Improved TD3 in Robotic Arm Control

The traditional robotic arm control method is based on the precise mathematical model of the task and lacks adaptability. When the environment or task changes, the control effect is greatly compromised or even out of control. In recent years, Deep Reinforcement Learning (DRL), which has achieved great success in games, has been introduced into the control of robotic arms. TD3 (Twin Delayed Deep Deterministic Policy Gradient) is an improved algorithm based on DDPG (Deep Deterministic Policy Gradient). Like other DRL algorithms, TD3 also has the problem of low learning efficiency. This paper proposes a improved TD3 algorithm which can converge faster than TD3 algorithm in terms of reachability and obstacle avoidance. Finally, the improvement of the algorithm is verified by a simulation research on a 6-DOF ABB-IRB1200 robotic arm.