A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation

Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 23; H. 7; S. 3762
Hauptverfasser: Han, Dong, Mulyana, Beni, Stankovic, Vladimir, Cheng, Samuel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 05.04.2023
MDPI
Schlagworte:
Actors
Actresses
Algorithms
Artificial intelligence
Automation
Computational linguistics
Data mining
Deep learning
graph neural network
Language processing
Machine learning
Manufacturing
Natural language interfaces
Neural networks
Portfolio management
Productivity
Recommender systems
reinforcement learning
Review
robotic manipulation
Robotics
Robotics industry
Robots
Search engines
Space exploration
Surveys
United States
robotic manipulation
graph neural network
reinforcement learning
ISSN:1424-8220, 1424-8220
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Zusammenfassung:Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor–critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23073762