Reinforcement learning for robot research: A comprehensive review and open issues

Applying the learning mechanism of natural living beings to endow intelligent robots with humanoid perception and decision-making wisdom becomes an important force to promote the revolution of science and technology in robot domains. Advances in reinforcement learning (RL) over the past decades have...

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Vydáno v:International journal of advanced robotic systems Ročník 18; číslo 3
Hlavní autoři: Zhang, Tengteng, Mo, Hongwei
Médium: Journal Article
Jazyk:angličtina
Vydáno: London, England SAGE Publications 01.05.2021
Sage Publications Ltd
SAGE Publishing
Témata:
Algorithms
Decision making
Domains
Humanoid
Machine learning
Robotics
Robots
dexterous manipulation
sim-to-real
mobile robotics
deep reinforcement learning
robotic applications
Reinforcement learning
ISSN:1729-8806, 1729-8814
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Shrnutí:Applying the learning mechanism of natural living beings to endow intelligent robots with humanoid perception and decision-making wisdom becomes an important force to promote the revolution of science and technology in robot domains. Advances in reinforcement learning (RL) over the past decades have led robotics to be highly automated and intelligent, which ensures safety operation instead of manual work and implementation of more intelligence for many challenging tasks. As an important branch of machine learning, RL can realize sequential decision-making under uncertainties through end-to-end learning and has made a series of significant breakthroughs in robot applications. In this review article, we cover RL algorithms from theoretical background to advanced learning policies in different domains, which accelerate to solving practical problems in robotics. The challenges, open issues, and our thoughts on future research directions of RL are also presented to discover new research areas with the objective to motivate new interest.
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ISSN:1729-8806
1729-8814
DOI:10.1177/17298814211007305