Machine-learning techniques have been widely applied for solving decision-making problems. Machine-learning algorithms perform better as compared to other algorithms while dealing with complex environments. The recent development in the area of neural network has enabled reinforcement learning techniques to provide the optimal policies for sophisticated and capable agents. In this paper, we would like to explore some algorithms people have applied recently based on interaction of multiple agents and their components. We would like to provide a survey of reinforcement-learning techniques to solve complex and real-world scenarios.
Revisited: Machine Intelligence in Heterogeneous Multi-Agent Systems
Lect. Notes Electrical Eng.
International Conference on Aerospace System Science and Engineering ; 2019 ; Toronto, ON, Canada July 30, 2019 - August 01, 2019
Proceedings of the International Conference on Aerospace System Science and Engineering 2019 ; Kapitel : 17 ; 219-229
01.03.2020
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Machine learning , Heterogeneous systems , Multi Agents , Q learning Physics , Space Sciences (including Extraterrestrial Physics, Space Exploration and Astronautics) , Aerospace Technology and Astronautics , Control and Systems Theory , Vibration, Dynamical Systems, Control , Engineering Design , Physics and Astronomy
Revisited: Machine Intelligence in Heterogeneous Multi-Agent Systems
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