关键词: 强化学习, 深度学习, 连续动作控制, 机器臂

Abstract: Deep reinforcement learning is good at solving the optimization problems of control. Because of the accuracy requirements, with the increasing of action dimension, the number of action increases exponentially. So, it is difficult to express the continuous action with discrete action. The Deep Deterministic Policy Gradient（DDPG） algorithm, based on the Actor-Critic framework, solves the problem of continuous motion control. But there are still some problems, such as the lack of scientific theory of sampling, the neglect of the differences between optimal action and non-optimal action when the action dimension is relatively high. In order to solve these problems, this paper presents an improved algorithm with optimal sampling and precise critic for DDPG algorithm. And it is successfully applied to the simulation of Selective Compliance Assembly Robot Arm（SCARA）. Compared with DDPG algorithm, an improvement effect is achieved and the SCARA robot is quickly and automatically positioned.

Key words: reinforcement learning, deep learning, continuous action control, robot arm