优化深度确定性策略梯度算法

深度强化学习善于解决控制的优化问题，连续动作的控制因为精度的要求，动作的数量随着动作维度的增加呈指数型增长，难以用离散的动作来表示。基于Actor-Critic框架的深度确定性策略梯度（Deep Deterministic Policy Gradient，DDPG）算法虽然解决了连续动作控制问题，但是仍然存在采样方式缺乏科学理论指导、动作维度较高时的最优动作与非最优动作之间差距被忽视等问题。针对上述问题，提出一种基于DDPG算法的优化采样及精确评价的改进算法，并成功应用于选择顺应性装配机器臂（Selective Compliance Assembly Robot Arm，SCARA）的仿真环境中，与原始的DDPG算法对比，取得了良好的效果，实现了SCARA机器人快速自动定位。

Optimized Deep Deterministic Policy Gradient Algorithm

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.