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CASIA OpenIR
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DIMSAN: Fast Exploration with the Synergy between Density-based Intrinsic Motivation and Self-adaptive Action Noise 会议论文
, 西安, 2021.5.30-2021.6.5
作者:  Li, Jiayi;  Li, Boyao;  Lu, Tao;  Lu, Ning;  Cai, Yinghao;  Wang, Shuo
Adobe PDF(5599Kb)  |  收藏  |  浏览/下载：201/39  |  提交时间：2022/06/14
Conservative Policy Gradient in Multi-critic Setting 会议论文
, Hangzhou, China, 2019.11.22-24
作者:  Xi, Bao;  Wang, Rui;  Wang, Shuo;  Lu, Tao;  Cai, Yinghao
浏览  |  Adobe PDF(379Kb)  |  收藏  |  浏览/下载：230/78  |  提交时间：2021/02/02
inconsistancy  stablility  Q learning  policy gradient  
Manipulation Skill Learning on Multi-step Complex Task Based on Explicit and Implicit Curriculum Learning 期刊论文
SCIENCE CHINA Information Sciences, 2020, 卷号: 0, 期号: 0, 页码: 0-0
作者:  Liu, Naijun;  Lu, Tao;  Cai, Yinghao;  Wang, Rui;  Wang, Shuo
浏览  |  Adobe PDF(2456Kb)  |  收藏  |  浏览/下载：196/81  |  提交时间：2020/09/27
robot  manipulation skill learning  multi-step complex task  curriculum learning  
Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning 会议论文
, Hefei, China, 2020
作者:  Liu, Naijun;  Lu, Tao;  Cai, Yinghao;  Wang, Rui;  Wang, Shuo
浏览  |  Adobe PDF(436Kb)  |  收藏  |  浏览/下载：181/63  |  提交时间：2020/09/27
ACDER: Augmented Curiosity-Driven Experience Replay 会议论文
, Paris, France, 2020.05.31-2020.08.31
作者:  Li, Boyao;  Lu, Tao;  Li, Jiayi;  Lu, Ning;  Cai, Yinghao;  Wang, Shuo
浏览  |  Adobe PDF(3303Kb)  |  收藏  |  浏览/下载：263/82  |  提交时间：2020/08/27
Curiosity-Driven Exploration for Off-Policy Reinforcement Learning Methods 会议论文
, Dali, China, 2019.12.06-2019.12.08
作者:  Li, Boyao;  Lu, Tao;  Li, Jiayi;  Lu, Ning;  Cai, Yinghao;  Wang, Shuo
浏览  |  Adobe PDF(2877Kb)  |  收藏  |  浏览/下载：211/74  |  提交时间：2020/08/27
An Automatic Robot Skills Learning System from Robot's Real-World Demonstrations 会议论文
, Nanchang, China, 2019.06.03-2019.06.05
作者:  Li, Boyao;  Lu, Tao;  Li, Xiaocan;  Cai, Yinghao;  Wang, Shuo
浏览  |  Adobe PDF(10072Kb)  |  收藏  |  浏览/下载：163/32  |  提交时间：2020/08/27
learn from demonstrations  simulation  real-world demonstrations  coordinate transformation  
Real-Sim-Real Transfer for Real-World Robot Control Policy Learning with Deep Reinforcement Learning 期刊论文
APPLIED SCIENCES-BASEL, 2020, 卷号: 10, 期号: 5, 页码: 16
作者:  Liu, Naijun;  Cai, Yinghao;  Lu, Tao;  Wang, Rui;  Wang, Shuo
浏览  |  Adobe PDF(6287Kb)  |  收藏  |  浏览/下载：270/67  |  提交时间：2020/06/02
robot  policy learning  reality gap  simulated environment  deep reinforcement learning