REAL-TIME REAL-WORLD REINFORCEMENT LEARNING SYSTEMS AND METHODS

A reinforcement learning architecture for facilitating reinforcement learning in connection with operation of an external real-time system that includes a plurality of devices operating in a real-world environment. The reinforcement learning architecture includes a plurality of communicators, a task...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	KOMER, Brent J, KORENKEVYCH, Dmytro, MAHMOOD, Ashique Rupam
Format:	Patent
Sprache:	eng ; fre ; ger
Schlagworte:	CALCULATING CHAMBERS PROVIDED WITH MANIPULATION DEVICES COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONTROL OR REGULATING SYSTEMS IN GENERAL CONTROLLING COUNTING ELECTRIC DIGITAL DATA PROCESSING FUNCTIONAL ELEMENTS OF SUCH SYSTEMS HAND TOOLS MANIPULATORS MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS PERFORMING OPERATIONS PHYSICS PORTABLE POWER-DRIVEN TOOLS REGULATING TRANSPORTING
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	A reinforcement learning architecture for facilitating reinforcement learning in connection with operation of an external real-time system that includes a plurality of devices operating in a real-world environment. The reinforcement learning architecture includes a plurality of communicators, a task manager, and a reinforcement learning agent that interact with each other to effectuate a policy for achieving a defined objective in the real-world environment. Each of the communicators receives sensory data from a corresponding device and the task manager generates a joint state vector based on the sensory data. The reinforcement learning agent generates, based on the joint state vector, a joint action vector, which the task manager parses into a plurality of actuation commands. The communicators transmit the actuation commands to the plurality of devices in the real-world environment.