Sungkyunkwan University

Gwang Pyo Yoo

Hong Uk Woo

Min Jong Yoo

Ik Jun Yeom

Seung Hwan Jeong

According to an exemplary embodiment of the present invention, a reinforcement learning method using a task decomposition inference model in a time-variant environment includes selecting a plurality of paired transitions having a time-invariant common characteristic and a time-variant different environmental characteristic from a dataset including a plurality of transition data, based on a cycle generative adversarial network (GAN), training an auto encoder to embed each of the time-variant part and the time-invariant part with respect to the plurality of paired transitions into a latent space, and performing reinforcement learning on a transition corresponding to data collected in the time-variant environment, using the trained auto encoder.

Reinforcement learning method and apparatus using task decomposition

A reinforcement learning-based sensor data management system includes a processor configured to: manage virtualized objects that correspond to sensors included in a sensor network to update data received from each sensor and queries representing a data quality requested by an application; calculate an abstracted action that abstracts a size of an action space of the sensor network based on present state information of the virtualized objects and the queries; calculate scores for virtualized objects based on position relationships between the calculated abstracted action the virtualized objects; and assign priorities to the virtualized objects based on the calculated scores to update data received from the sensors to the virtualized objects according to the priorities.

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Suwon-si, South Korea

Gwang Pyo Yoo