CASIA OpenIR  > 智能机器人系统研究
ACDER: Augmented Curiosity-Driven Experience Replay
Li, Boyao1,2; Lu, Tao2; Li, Jiayi1,2; Lu, Ning1,2; Cai, Yinghao2; Wang, Shuo1,2
2020-08
会议名称IEEE International Conference on Robotics and Automation
会议日期2020.05.31-2020.08.31
会议地点Paris, France
摘要

Exploration in environments with sparse feedback remains a challenging research problem in reinforcement learning (RL). When the RL agent explores the environment randomly, it results in low exploration efficiency, especially in robotic manipulation tasks with high dimensional continuous state and action space. In this paper, we propose a novel method, called Augmented Curiosity-Driven Experience Replay (ACDER), which leverages (i) a new goal-oriented curiositydriven exploration to encourage the agent to pursue novel and task-relevant states more purposefully and (ii) the dynamic initial states selection as an automatic exploratory curriculum to further improve the sample-efficiency. Our approach complements Hindsight Experience Replay (HER) by introducing a new way to pursue valuable states. Experiments conducted on four challenging robotic manipulation tasks with binary rewards, including Reach, Push, Pick&Place and Multi-step Push. The empirical results show that our proposed method significantly outperforms existing methods in the first three basic tasks and also achieves satisfactory performance in multistep robotic task learning.
 

收录类别EI
语种英语
七大方向——子方向分类智能机器人
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/40236
专题智能机器人系统研究
通讯作者Lu, Tao
作者单位1.中国科学院大学
2.中国科学院自动化研究所
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Li, Boyao,Lu, Tao,Li, Jiayi,et al. ACDER: Augmented Curiosity-Driven Experience Replay[C],2020.
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