Авторы: 
Alfimtsev Alexander Nikolaevich
Pitikin Alexey Ruslanovich
Дата поступления: 
03.03.2023
Библиографическое описание статьи: 

Alfimtsev A.N., Pitikin A.R. Emergency properties of multi-agent reinforcement learning // Informacionnye tehnologii i matematicheskoe modelirovanie v upravlenii slozhnymi sistemami: elektronnyj nauchnyj zhurnal [Information technology and mathematical modeling in the management of complex systems: electronic scientific journal], 2023. No. 1(17). P. 1-10. DOI: 10.26731/2658‑3704.2023.1(17).1-10 [Accessed 31/03/23]

Рубрика: 
1. Mathematical problems of modeling systems
Год: 
2023
Номер журнала (Том): 
1(17) 2023
УДК: 
004.853
DOI: 

10.26731/2658‑3704.2023.1(17).1-10

Файл статьи: 
Иконка PDF emergency_properties.pdf
Страницы: 
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10
Аннотация: 

This paper presents ten emergent properties of multi-agent reinforcement learning. Each property is formalized using Markov decision processes and presented as a formula. It has been suggested that such a formalization will allow further targeted training of a multi-agent system to obtain the necessary emergent properties. It has been established that emergence in multi-agent reinforcement learning is weak. Highly cited publications on the topic of multi-agent learning were analyzed in order to check the presence of the formulated properties. Based on the results of the work done, a summary table of properties is presented indicating the algorithm in which the property was discovered, the environment for which the algorithm was created, the architecture of the agent's neural network, and the reinforcement learning scheme used.

Ключевые слова: 
artificial intelligence
deep machine learning
reinforcement learning
multi-agent system
application software
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