Abstract: A generative adversarial network-based classification system and method that can generate missing data as missing data imputation values similar to real data using a generative adversarial network (GAN) and allowing training with labeled data sets with labels, as well as and irregular data sets such as non-labeled data sets without labels.

Abstract: The present invention relates to a deep-learning based system and method of automatically determining a degree of damage to each area of a vehicle, which is capable of quickly calculating a consistent and reliable quote for vehicle repair by analyzing an image of a vehicle in an accident by using a deep learning-based Mark R-CNN framework and then extracting a component image corresponding to a damaged part, and automatically determining the degree of damage in the extracted component image based on a pre-trained model.

Abstract: The present disclosure may provide parameterized hyperparameter partitioning in consideration of balance in partition size while preserving a property of a hypergraph necessary to apply deep reinforcement learning by reducing the large-size hypergraph, and may reduce the computational amount and capacity of an artificial neural network by reducing a graph.

Abstract: Disclosed are an apparatus and a method for reinforcement learning for semiconductor element position optimization based on semiconductor design data. According to the present disclosure, a learning environment may be constructed based on a user's semiconductor design data such that optimal positions of semiconductor elements are provided during a semiconductor design process through reinforcement learning using simulation.

Abstract: Disclosed are a reinforcement learning device and method using a conditional episode configuration. The present invention imparts conditions on individual decision making, and terminates an episode if the imparted conditions are not met, thereby maximizing the total sum of rewards reflecting the current values. Accordingly, reinforcement learning can be easily applied even to problems using a non-continuous state.

Abstract: Disclosed are an apparatus and a method for reinforcement learning based on a user learning environment in semiconductor design. According to the present disclosure, a user may configure a learning environment in semiconductor design and may determine optimal positions of semiconductor elements and standard cells through reinforcement learning using simulation, and reinforcement learning may be performed based on the learning environment configured by the user, thereby automatically determining optimized semiconductor element positions in various environments.

Abstract: Disclosed is a user learning environment-based reinforcement learning apparatus and method. According to the disclosure, a CAD data based-reinforcement learning environment may be easily set by a user using a user interface (UI) and a drag and drop, a reinforcement learning environment may be promptly configured, and reinforcement learning may be performed based on the learning environment set by the user, and thus the optimized location of a target object may be automatically produced in various environments.

Abstract: Disclosed are a reinforcement learning apparatus and a reinforcement learning method for optimizing the position of an object based on design data. The present disclosure may configure a learning environment based on design data of a user and generate the optimal position of a target object, installed around a specific object during a design or manufacturing process, through reinforcement learning using simulation.

Abstract: Disclosed is a deep reinforcement learning apparatus and method for a pick-and-place system. According to the present disclosure, a simulation learning framework is configured to apply reinforcement learning to make pick-and-place decisions using a robot operating system (ROS) in a real-time environment, thereby generating stable path motion that meets various hardware and real-time constraints.

Abstract: Disclosed is a device for data-based reinforcement learning. The disclosure allows an agent to learn a reinforcement learning model so as to maximize a reward for an action selectable according to a current state in a random environment, wherein a difference between a total variation rate and an individual variation rate for each action is provided as a reward for the agent.

Abstract: Disclosed are generative adversarial network-based classification system and method. The present invention can generate missing data as missing data imputation values similar to real data using a generative adversarial network (GAN), thus allowing the overall quality of the data to be improved, and allowing training with labeled data sets with labels, as well as irregular data sets such as non-labeled data sets without labels.

Abstract: Disclosed is a decision-making agent having a hierarchical structure. The present invention allows a user without knowledge about reinforcement learning to learn by easily setting and applying core factors of the reinforcement learning to business problems.

Abstract: The present invention relates to a method and a system for training a model for automatically determining the degree of damage for each vehicle area based on deep learning, which generate a model capable of quickly calculating a consistent and reliable vehicle repair quote by learning so as to automatically extract a picture in which it is possible to determine the degree of damage among accident vehicle pictures by using the Mask R-CNN framework and the Inception V4 network structure based on deep learning, and learning the degree of damage for each type of damage.

Abstract: The present invention relates to a deep-learning based system and method of automatically determining a degree of damage to each area of a vehicle, which is capable of quickly calculating a consistent and reliable quote for vehicle repair by analyzing an image of a vehicle in an accident by using a deep learning-based Mark R-CNN framework and then extracting a component image corresponding to a damaged part, and automatically determining the degree of damage in the extracted component image based on a pre-trained model.