Real–Sim–Real Transfer for Real-World Robot Control Policy Learning with Deep Reinforcement Learning

Real–Sim–Real Transfer for Real-World Robot Control Policy Learning with Deep Reinforcement Learning

Compared to traditional data-driven learning methods, recently developed deep reinforcement learning (DRL) approaches can be employed to train robot agents to obtain control policies with appealing performance. However, learning control policies for real-world robots through DRL is costly and cumber...

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Bibliographic Details
Main Authors: Naijun Liu, Yinghao Cai, Tao Lu, Rui Wang, Shuo Wang
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Applied Sciences
Subjects:
robot
policy learning
reality gap
simulated environment
deep reinforcement learning
Online Access:https://www.mdpi.com/2076-3417/10/5/1555

Internet

https://www.mdpi.com/2076-3417/10/5/1555

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