Abstract: A target-oriented reinforcement learning method according to an embodiment includes: collecting data related to the target of reinforcement learning as target data in the process of performing the reinforcement learning; learning the collected target data as auxiliary learning for the reinforcement learning; and incorporating the results of the learning of the target data into the performance of the reinforcement learning.

Abstract: Disclosed herein are a multi-target analysis apparatus and method. The multi-target analysis apparatus includes: an input/output interface configured to receive data and output the results of computation of the data; storage configured to store a program for performing a multi-target analysis method; and a controller provided with at least one process, and configured to analyze multiple targets received through the input/output interface by executing the program. The controller is further configured to: collect instruction-target pairs in each of which an instruction and state information for a target are matched with each other so that the target is specified through the instruction, and generate an instruction-target set having a plurality of instruction-target pairs; and train a reinforcement learning-based learning model configured to receive the instruction for the target and the state information for the target and output action information by referring to the instruction-target set.

Abstract: Disclosed herein are a computing apparatus and method for performing reinforcement learning using a multimodal artificial intelligence agent. The method for performing reinforcement learning using a multimodal artificial intelligence agent includes: dividing frames, included in images acquired by capturing a virtual environment, into a plurality of sections; and performing reinforcement learning by applying any one of a plurality of guidance types to each of the plurality of sections and then allowing a multimodal artificial intelligence agent to interact with the virtual environment through the images. The plurality of guidance types is classified into three or more types according to their guidance level. Performing the reinforcement learning is performing reinforcement learning by applying a moderate-level guidance type to the sections of predetermined critical periods and also applying any one of the plurality of guidance types to the other sections.

Abstract: A target-oriented reinforcement learning method according to an embodiment includes: collecting data related to the target of reinforcement learning as target data in the process of performing the reinforcement learning; learning the collected target data as auxiliary learning for the reinforcement learning; and incorporating the results of the learning of the target data into the performance of the reinforcement learning.