ATTENTION-BASED DEEP REINFORCEMENT LEARNING FOR AUTONOMOUS AGENTS

ATTENTION-BASED DEEP REINFORCEMENT LEARNING FOR AUTONOMOUS AGENTS

A data source configured to provide a representation of an environment of one or more agents is identified. Using a data set obtained from the data source, a neural network-based reinforcement learning model with one or more attention layers is trained. Importance indicators generated by the attenti...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Bodapati, Sravan Babu, Sun, Tao, Kasaragod, Sunil Mallya, Genc, Sahika
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Beschreibung
Zusammenfassung:A data source configured to provide a representation of an environment of one or more agents is identified. Using a data set obtained from the data source, a neural network-based reinforcement learning model with one or more attention layers is trained. Importance indicators generated by the attention layers are used to identify actions to be initiated by an agent. A trained version of the model is stored.