2020
DOI: 10.23939/acps2020.02.050
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Decentralized Control of Adaptive Data Collection Processes Based on Equilibrium Concept and Reinforcement Learning

Abstract: The model of decentralized control of adaptive data collection processes has been developed based on the equilibrium concept, which is used to study the problem of coordinating joint collective actions from the point of view of finding an effective scheme for complementing the individual actions of data collection processes in the absence of a control center. The method of decentralized control of adaptive data collection processes in autonomous distributed systems based on the equilibrium concept and reinforc… Show more

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Cited by 1 publication
(2 citation statements)
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“…The environment is usually presented as a Markov decision-making process (MDP), as many learning algorithms use dynamic programming techniques to reinforce this context. The main difference between classical active programming methods and supported learning algorithms is that the latter does not require knowledge of the exact mathematical model of MDP, and they target large MDPs where accurate methods become impossible [6].…”
Section: Fig 3 Diagram Of the Interaction Between The Components Of T...mentioning
confidence: 99%
See 1 more Smart Citation
“…The environment is usually presented as a Markov decision-making process (MDP), as many learning algorithms use dynamic programming techniques to reinforce this context. The main difference between classical active programming methods and supported learning algorithms is that the latter does not require knowledge of the exact mathematical model of MDP, and they target large MDPs where accurate methods become impossible [6].…”
Section: Fig 3 Diagram Of the Interaction Between The Components Of T...mentioning
confidence: 99%
“…Decentralization [6] is the foundation that gives companies more independence over their collaboration, data ownership, better privacy and security, a more robust and resilient communications infrastructure, and massive scalability.…”
Section: Introductionmentioning
confidence: 99%