Abstract
The paper considers integrated tools consist of multi-agent temporal differences reinforcement learning, statistical and main analysis modules. Deep reinforcement learning approach were analyzed to improve performance of reinforcement learning algorithms under time constraints. The possibilities of including anytime algorithm, particularly milestone method, into the forecasting subsystem type of intelligent decision support system of real-time for improving performance and reducing response and execution time were proposed. The work was supported by RFBR projects 17-07-00553, 18-51-00007.
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Eremeev, A.P., Kozhukhov, A.A. (2019). About the Integration of Learning and Decision-Making Models in Intelligent Systems of Real-Time. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). IITI'18 2018. Advances in Intelligent Systems and Computing, vol 875. Springer, Cham. https://doi.org/10.1007/978-3-030-01821-4_19
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DOI: https://doi.org/10.1007/978-3-030-01821-4_19
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