REINFORCEMENT LEARNING FOR OPTIMIZING CROSS-CHANNEL COMMUNICATIONS

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing agent actions using a predictive software agent framework configured to retrieve historical event sequence data, transform, using a state encoder m...

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Bibliographic Details
Main Authors Weinberg, Jason Edward, Hui, Man Kin
Format Patent
LanguageEnglish
Published 29.02.2024
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing agent actions using a predictive software agent framework configured to retrieve historical event sequence data, transform, using a state encoder machine learning model, the historical event sequence data into one or more sequence embeddings comprising fixed-length vectors, generate, using a predictive software agent machine learning model, a prediction output comprising one or more optimal agent actions comprising at least a best agent action based on the one or more sequence embeddings.
Bibliography:Application Number: US202318168774