Reinforcement learning in robotic applications: a comprehensive survey

In recent trends, artificial intelligence (AI) is used for the creation of complex automated control systems. Still, researchers are trying to make a completely autonomous system that resembles human beings. Researchers working in AI think that there is a strong connection present between the learni...

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Vydáno v:The Artificial intelligence review Ročník 55; číslo 2; s. 945 - 990
Hlavní autoři: Singh, Bharat, Kumar, Rajesh, Singh, Vinay Pratap
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.02.2022
Springer
Springer Nature B.V
Témata:
Algorithms
Application
Artificial Intelligence
Automatic control
Brain
Complex tasks
Computer Science
Control systems
Forecasts and trends
Humans
Machine learning
Multiagent systems
Platforms
Polls & surveys
Reinforcement
Robotics
Robots
Self instruction
System effectiveness
Task complexity
Water
Water supply
Actor-critic methods
Neuro-evolution
Reinforcement learning
Multi-agent RL
Human–robot interaction
Robotics
DeepRL
ISSN:0269-2821, 1573-7462
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Shrnutí:In recent trends, artificial intelligence (AI) is used for the creation of complex automated control systems. Still, researchers are trying to make a completely autonomous system that resembles human beings. Researchers working in AI think that there is a strong connection present between the learning pattern of human and AI. They have analyzed that machine learning (ML) algorithms can effectively make self-learning systems. ML algorithms are a sub-field of AI in which reinforcement learning (RL) is the only available methodology that resembles the learning mechanism of the human brain. Therefore, RL must take a key role in the creation of autonomous robotic systems. In recent years, RL has been applied on many platforms of the robotic systems like an air-based, under-water, land-based, etc., and got a lot of success in solving complex tasks. In this paper, a brief overview of the application of reinforcement algorithms in robotic science is presented. This survey offered a comprehensive review based on segments as (1) development of RL (2) types of RL algorithm like; Actor-Critic, DeepRL, multi-agent RL and Human-centered algorithm (3) various applications of RL in robotics based on their usage platforms such as land-based, water-based and air-based, (4) RL algorithms/mechanism used in robotic applications. Finally, an open discussion is provided that potentially raises a range of future research directions in robotics. The objective of this survey is to present a guidance point for future research in a more meaningful direction.
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-021-09997-9