{"title":"深度强化学习算法在机器人机械手控制中的应用比较","authors":"Chang Chu, Kazuhiko Takahashi, M. Hashimoto","doi":"10.1109/IS3C50286.2020.00080","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":143430,"journal":{"name":"2020 International Symposium on Computer, Consumer and Control (IS3C)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Comparison of Deep Reinforcement Learning Algorithms in a Robot Manipulator Control Application\",\"authors\":\"Chang Chu, Kazuhiko Takahashi, M. Hashimoto\",\"doi\":\"10.1109/IS3C50286.2020.00080\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":143430,\"journal\":{\"name\":\"2020 International Symposium on Computer, Consumer and Control (IS3C)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Symposium on Computer, Consumer and Control (IS3C)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IS3C50286.2020.00080\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Symposium on Computer, Consumer and Control (IS3C)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IS3C50286.2020.00080","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.