深度强化学习算法在机器人机械手控制中的应用比较

Chang Chu, Kazuhiko Takahashi, M. Hashimoto
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引用次数: 3

摘要

在本研究中,我们将深度强化学习(DRL)应用于机器人机械手的控制,并通过比较几种DRL算法(即深度确定性策略梯度(DDPG)和分布式确定性策略梯度(D4PG)算法的性能来研究其有效性。针对机器人机械手伸手任务的控制模型进行了计算训练和测试实验。实验结果表明,D4PG算法比DDPG算法具有更高的学习成功率,证明了DRL在机械臂控制方面的潜在应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Deep Reinforcement Learning Algorithms in a Robot Manipulator Control Application
In this study, we apply deep reinforcement learning (DRL) to control a robot manipulator and investigate its effectiveness by comparing the performance of several DRL algorithms, namely, deep deterministic policy gradient (DDPG) and distributed distributional deterministic policy gradient (D4PG) algorithms. We conducted computational training and testing experiments on a control model for a reaching task of the robot manipulator. Experimental results show that the D4PG algorithm achieves a higher learning success rate than the DDPG algorithm and demonstrate the potential application of DRL for controlling robot manipulators.
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